On the public citation graph

A Machine Learning-Based Method To Improve Docking Scoring Functions and Its Application to Drug Repurposing

0 signed · 278 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/anie.200300644openalex-snapshot

Understanding Noncovalent Interactions: Ligand Binding Energy and Catalytic Efficiency from Ligand‐Induced Reductions in Motion within Receptors and Enzymes

inferredcites →https://doi.org/10.1002/cmdc.200800261openalex-snapshot

Triclosan Derivatives: Towards Potent Inhibitors of Drug‐Sensitive and Drug‐Resistant Mycobacterium tuberculosis

inferredcites →https://doi.org/10.1007/s10822-007-9167-2openalex-snapshot

Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection?

inferredcites →https://doi.org/10.1007/s10822-008-9181-zopenalex-snapshot

Managing bias in ROC curves

inferredcites →https://doi.org/10.1007/s10822-008-9189-4openalex-snapshot

Community benchmarks for virtual screening

inferredcites →https://doi.org/10.1007/s10822-008-9192-9openalex-snapshot

Utilizing high throughput screening data for predictive toxicology models: protocols and application to MLSCN assays

inferredcites →https://doi.org/10.1016/j.jmb.2007.06.002openalex-snapshot

Predicting Absolute Ligand Binding Free Energies to a Simple Model Site

inferredcites →https://doi.org/10.1016/j.jmb.2010.02.007openalex-snapshot

Non-additivity of Functional Group Contributions in Protein–Ligand Binding: A Comprehensive Study by Crystallography and Isothermal Titration Calorimetry

inferredcites →https://doi.org/10.1016/j.jmgm.2006.06.002openalex-snapshot

eHiTS: A new fast, exhaustive flexible ligand docking system

inferredcites →https://doi.org/10.1016/j.jmgm.2008.08.004openalex-snapshot

Data mining PubChem using a support vector machine with the Signature molecular descriptor: Classification of factor XIa inhibitors

inferredcites →https://doi.org/10.1016/j.str.2009.02.010openalex-snapshot

Binding of Small-Molecule Ligands to Proteins: “What You See” Is Not Always “What You Get”

inferredcites →https://doi.org/10.1016/s0169-409x(00)00129-0openalex-snapshot

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1

inferredcites →https://doi.org/10.1021/ci034246+openalex-snapshot

Predicting Protein−Ligand Binding Affinities Using Novel Geometrical Descriptors and Machine-Learning Methods

inferredcites →https://doi.org/10.1021/ci049714+openalex-snapshot

ZINC − A Free Database of Commercially Available Compounds for Virtual Screening

inferredcites →https://doi.org/10.1021/ci100244vopenalex-snapshot

NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes

inferredcites →https://doi.org/10.1021/ci700157bopenalex-snapshot

y-Randomization and Its Variants in QSPR/QSAR

inferredcites →https://doi.org/10.1021/ci800110popenalex-snapshot

FieldScreen: Virtual Screening Using Molecular Fields. Application to the DUD Data Set

inferredcites →https://doi.org/10.1021/ci800298zopenalex-snapshot

Empirical Scoring Functions for Advanced Protein−Ligand Docking with PLANTS

inferredcites →https://doi.org/10.1021/ci8004226openalex-snapshot

How To Optimize Shape-Based Virtual Screening: Choosing the Right Query and Including Chemical Information

inferredcites →https://doi.org/10.1021/ci900204yopenalex-snapshot

LigMatch: A Multiple Structure-Based Ligand Matching Method for 3D Virtual Screening

inferredcites →https://doi.org/10.1021/ci900382eopenalex-snapshot

Combining Machine Learning and Pharmacophore-Based Interaction Fingerprint for in Silico Screening

inferredcites →https://doi.org/10.1021/jm0306430openalex-snapshot

Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy

inferredcites →https://doi.org/10.1021/jm0493360openalex-snapshot

PostDOCK: A Structural, Empirical Approach to Scoring Protein Ligand Complexes

inferredcites →https://doi.org/10.1021/jm050040jopenalex-snapshot

Parameter Estimation for Scoring Protein−Ligand Interactions Using Negative Training Data

inferredcites →https://doi.org/10.1021/jm050362nopenalex-snapshot

A Critical Assessment of Docking Programs and Scoring Functions

inferredcites →https://doi.org/10.1021/jm060715yopenalex-snapshot

Pyrrolidine Carboxamides as a Novel Class of Inhibitors of Enoyl Acyl Carrier Protein Reductase from Mycobacterium tuberculosis

inferredcites →https://doi.org/10.1021/jm0608356openalex-snapshot

Benchmarking Sets for Molecular Docking

inferredcites →https://doi.org/10.1021/jm9009444openalex-snapshot

Structure-Based Optimization of Potent and Selective Inhibitors of the Tyrosine Kinase Erythropoietin Producing Human Hepatocellular Carcinoma Receptor B4 (EphB4)

inferredcites →https://doi.org/10.1021/jm9016416openalex-snapshot

Enhancement of Hydrophobic Interactions and Hydrogen Bond Strength by Cooperativity: Synthesis, Modeling, and Molecular Dynamics Simulations of a Congeneric Series of Thrombin Inhibitors

inferredcites →https://doi.org/10.1021/jp0217839openalex-snapshot

Absolute Binding Free Energies: A Quantitative Approach for Their Calculation

inferredcites →https://doi.org/10.1023/a:1007996124545openalex-snapshot

Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes

inferredcites →https://doi.org/10.1063/1.2989800openalex-snapshot

Calculation of absolute protein-ligand binding free energy using distributed replica sampling

inferredcites →https://doi.org/10.1073/pnas.0409005102openalex-snapshot

Calculation of absolute protein–ligand binding free energy from computer simulations

inferredcites →https://doi.org/10.1073/pnas.0704422105openalex-snapshot

Detecting evolutionary relationships across existing fold space, using sequence order-independent profile–profile alignments

inferredcites →https://doi.org/10.1073/pnas.162365999openalex-snapshot

On the calculation of absolute macromolecular binding free energies

inferredcites →https://doi.org/10.1074/jbc.274.22.15582openalex-snapshot

Crystal Structure of the Mycobacterium tuberculosis Enoyl-ACP Reductase, InhA, in Complex with NAD+ and a C16 Fatty Acyl Substrate

inferredcites →https://doi.org/10.1074/jbc.m211968200openalex-snapshot

Targeting Tuberculosis and Malaria through Inhibition of Enoyl Reductase

inferredcites →https://doi.org/10.1093/bioinformatics/btg1086openalex-snapshot

Flexible structure alignment by chaining aligned fragment pairs allowing twists

inferredcites →https://doi.org/10.1093/bioinformatics/btp220openalex-snapshot

A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery

inferredcites →https://doi.org/10.1093/bioinformatics/btp589openalex-snapshot

A novel method for mining highly imbalanced high-throughput screening data in PubChem

inferredcites →https://doi.org/10.1093/bioinformatics/btq112openalex-snapshot

A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

inferredcites →https://doi.org/10.1093/nar/28.1.235openalex-snapshot

The Protein Data Bank

inferredcites →https://doi.org/10.1093/nar/gkl999openalex-snapshot

BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities

inferredcites →https://doi.org/10.1093/protein/6.7.723openalex-snapshot

Matching chemistry and shape in molecular docking

inferredcites →https://doi.org/10.1109/grc.2006.1635839openalex-snapshot

Granular SVM with Repetitive Undersampling for Highly Imbalanced Protein Homology Prediction

inferredcites →https://doi.org/10.1109/tsmcb.2008.2002909openalex-snapshot

SVMs Modeling for Highly Imbalanced Classification

inferredcites →https://doi.org/10.1186/1471-2105-8-s4-s9openalex-snapshot

A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand binding sites

inferredcites →https://doi.org/10.1186/1471-2105-9-401openalex-snapshot

Developing and validating predictive decision tree models from mining chemical structural fingerprints and high–throughput screening data in PubChem

inferredcites →https://doi.org/10.1371/journal.pcbi.1000423openalex-snapshot

Drug Discovery Using Chemical Systems Biology: Repositioning the Safe Medicine Comtan to Treat Multi-Drug and Extensively Drug Resistant Tuberculosis

inferredcites →https://doi.org/10.1371/journal.pcbi.1000976openalex-snapshot

The Mycobacterium tuberculosis Drugome and Its Polypharmacological Implications

inferredcites →https://doi.org/10.1529/biophysj.106.084301openalex-snapshot

Absolute Binding Free Energy Calculations Using Molecular Dynamics Simulations with Restraining Potentials

inferredcites →https://doi.org/10.17877/de290r-14262openalex-snapshot

Making Large-Scale SVM Learning Practical

inferredcites →https://doi.org/10.2174/138955710791185064openalex-snapshot

Recent Progress in the Identification and Development of InhA Direct Inhibitors of Mycobacterium tuberculosis

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/0471266949.bmc301openalex-snapshot

Docking, Scoring, and Virtual Screening in Drug Discovery

inferred← citeshttps://doi.org/10.1002/9781119654803.ch5openalex-snapshot

In Silico Protein Design and Virtual Screening

inferred← citeshttps://doi.org/10.1002/9781119681397.ch23openalex-snapshot

In SilicoPlatforms for Predictive Ecotoxicology

inferred← citeshttps://doi.org/10.1002/9781394167258.ch1openalex-snapshot

Molecular Recognition and Machine Learning to Predict Protein‐Ligand Interactions

inferred← citeshttps://doi.org/10.1002/9781394167258.ch2openalex-snapshot

Machine Learning Approaches to Improve Prediction of Target‐Drug Interactions

inferred← citeshttps://doi.org/10.1002/9781394167258.ch6openalex-snapshot

Recent Advances in Drug Design With Machine Learning

inferred← citeshttps://doi.org/10.1002/9783527645947.ch12openalex-snapshot

Scoring Functions for Protein–Ligand Interactions

inferred← citeshttps://doi.org/10.1002/adma.201500966openalex-snapshot

Understanding Small‐Molecule Interactions in Metal–Organic Frameworks: Coupling Experiment with Theory

inferred← citeshttps://doi.org/10.1002/ctd2.70195openalex-snapshot

Computational strategies to address therapeutic resistance in estrogen receptor‐positive breast cancer: A narrative review of molecular docking, molecular dynamics simulations, and emerging therapeutic targets

inferred← citeshttps://doi.org/10.1002/ddr.21211openalex-snapshot

Opportunities and Challenges Provided by Cloud Repositories for Bioinformatics‐Enabled Drug Discovery

inferred← citeshttps://doi.org/10.1002/jcc.24031openalex-snapshot

GeauxDock: A novel approach for mixed‐resolution ligand docking using a descriptor‐based force field

inferred← citeshttps://doi.org/10.1002/jmr.2266openalex-snapshot

Latest developments in molecular docking: 2010–2011 in review

inferred← citeshttps://doi.org/10.1002/med.21764openalex-snapshot

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

inferred← citeshttps://doi.org/10.1002/minf.201400132openalex-snapshot

Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets

inferred← citeshttps://doi.org/10.1002/minf.201600048openalex-snapshot

Empirical Scoring Functions for Affinity Prediction of Protein‐ligand Complexes

inferred← citeshttps://doi.org/10.1002/wcms.1183openalex-snapshot

Machine learning methods in chemoinformatics

inferred← citeshttps://doi.org/10.1002/wcms.1225openalex-snapshot

Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening

inferred← citeshttps://doi.org/10.1002/wcms.1429openalex-snapshot

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

inferred← citeshttps://doi.org/10.1002/wcms.1567openalex-snapshot

Featurization strategies for protein–ligand interactions and their applications in scoring function development

inferred← citeshttps://doi.org/10.1002/widm.1451openalex-snapshot

Machine learning in postgenomic biology and personalized medicine

inferred← citeshttps://doi.org/10.1007/978-1-0716-1787-8_1openalex-snapshot

Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges

inferred← citeshttps://doi.org/10.1007/978-1-0716-4949-7_6openalex-snapshot

Decision Tree for Prediction of Binding Affinity

inferred← citeshttps://doi.org/10.1007/978-1-4939-9752-7_1openalex-snapshot

Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity

inferred← citeshttps://doi.org/10.1007/978-3-030-05282-9_7openalex-snapshot

Recent Advancements in Computing Reliable Binding Free Energies in Drug Discovery Projects

inferred← citeshttps://doi.org/10.1007/978-3-030-48283-1_5openalex-snapshot

Developing Kinase Inhibitors Using Computer-Aided Drug Design Approaches

inferred← citeshttps://doi.org/10.1007/978-3-030-58080-3_74-1openalex-snapshot

Ageing and Alzheimer’s Disease

inferred← citeshttps://doi.org/10.1007/978-3-030-64573-1_74openalex-snapshot

Aging and Alzheimer’s Disease

inferred← citeshttps://doi.org/10.1007/978-3-031-75984-0_14openalex-snapshot

The Role of Artificial Intelligence in the Discovery of New Drugs

inferred← citeshttps://doi.org/10.1007/978-3-031-81728-1_27openalex-snapshot

Molecular Dynamics in Predicting the Stability of Drug-Receptor Interactions

inferred← citeshttps://doi.org/10.1007/978-3-642-34123-6_2openalex-snapshot

Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression

inferred← citeshttps://doi.org/10.1007/978-981-10-5187-6_9openalex-snapshot

Cheminformatics Approaches in Modern Drug Discovery

inferred← citeshttps://doi.org/10.1007/978-981-15-5534-3_6openalex-snapshot

Computer-Aided Drug Design

inferred← citeshttps://doi.org/10.1007/978-981-16-0811-7_9openalex-snapshot

Use of Artificial Intelligence in Research and Clinical Decision Making for Combating Mycobacterial Diseases

inferred← citeshttps://doi.org/10.1007/978-981-95-9081-0_11openalex-snapshot

Strategic Pathways for Future GPCR Research and Development

inferred← citeshttps://doi.org/10.1007/978-981-97-5128-0_1openalex-snapshot

Predicting Drug-Target Affinity Using Protein Pocket and Graph Convolution Network

inferred← citeshttps://doi.org/10.1007/s00214-017-2083-1openalex-snapshot

Feature functional theory–binding predictor (FFT–BP) for the blind prediction of binding free energies

inferred← citeshttps://doi.org/10.1007/s00894-012-1416-1openalex-snapshot

FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols

inferred← citeshttps://doi.org/10.1007/s10462-021-10058-4openalex-snapshot

Machine Learning in Drug Discovery: A Review

inferred← citeshttps://doi.org/10.1007/s10822-011-9482-5openalex-snapshot

Improving molecular docking through eHiTS’ tunable scoring function

inferred← citeshttps://doi.org/10.1007/s10822-013-9670-6openalex-snapshot

Experimental versus predicted affinities for ligand binding to estrogen receptor: iterative selection and rescoring of docked poses systematically improves the correlation

inferred← citeshttps://doi.org/10.1007/s10822-017-0068-8openalex-snapshot

Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization

inferred← citeshttps://doi.org/10.1007/s10822-019-00247-3openalex-snapshot

Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S

inferred← citeshttps://doi.org/10.1007/s10822-021-00423-4openalex-snapshot

Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction

inferred← citeshttps://doi.org/10.1007/s10822-026-00824-3openalex-snapshot

Dynamic stability–driven machine learning improves binding pose identification on human serum albumin

inferred← citeshttps://doi.org/10.1007/s10822-026-00923-1openalex-snapshot

AI-driven drug design: a comprehensive review

inferred← citeshttps://doi.org/10.1007/s11030-021-10280-wopenalex-snapshot

Improved machine learning scoring functions for identification of Electrophorus electricus’s acetylcholinesterase inhibitors

inferred← citeshttps://doi.org/10.1007/s11030-022-10489-3openalex-snapshot

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

inferred← citeshttps://doi.org/10.1007/s11095-018-2558-3openalex-snapshot

High Throughput and Computational Repurposing for Neglected Diseases

inferred← citeshttps://doi.org/10.1007/s11427-018-9342-2openalex-snapshot

Artificial intelligence in drug design

inferred← citeshttps://doi.org/10.1007/s12539-019-00327-wopenalex-snapshot

An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking

inferred← citeshttps://doi.org/10.1007/s12539-023-00582-yopenalex-snapshot

A Multi-perspective Model for Protein–Ligand-Binding Affinity Prediction

inferred← citeshttps://doi.org/10.1007/s12639-026-01994-5openalex-snapshot

Discovery of protein–ligand interactions based on molecular docking for identifying potential drug candidates against Leishmania spp.

inferred← citeshttps://doi.org/10.1016/b978-0-12-802808-7.00012-5openalex-snapshot

Drug Repurposing for Epigenetic Targets Guided by Computational Methods

inferred← citeshttps://doi.org/10.1016/b978-0-12-809479-2.00012-3openalex-snapshot

Big Drug Design Mechanisms via Systems Biology and Big Database Mining

inferred← citeshttps://doi.org/10.1016/b978-0-12-816125-8.00015-8openalex-snapshot

Application of In Silico Drug Repurposing in Infectious Diseases

inferred← citeshttps://doi.org/10.1016/b978-0-12-821748-1.00001-4openalex-snapshot

Impact of chemoinformatics approaches and tools on current chemical research

inferred← citeshttps://doi.org/10.1016/b978-0-12-822312-3.00004-7openalex-snapshot

Post-processing of Docking Results: Tools and Strategies

inferred← citeshttps://doi.org/10.1016/b978-0-12-822312-3.00009-6openalex-snapshot

Design and Discovery of Kinase Inhibitors Using Docking Studies

inferred← citeshttps://doi.org/10.1016/b978-0-12-822312-3.00011-4openalex-snapshot

Modern Tools and Techniques in Computer-Aided Drug Design

inferred← citeshttps://doi.org/10.1016/b978-0-443-18638-7.00020-7openalex-snapshot

Machine learning methods in drug design

inferred← citeshttps://doi.org/10.1016/b978-0-443-49253-2.00007-6openalex-snapshot

Drug repurposing using docking approaches

inferred← citeshttps://doi.org/10.1016/bs.armc.2022.08.008openalex-snapshot

Can docking scoring functions guarantee success in virtual screening?

inferred← citeshttps://doi.org/10.1016/j.abb.2020.108730openalex-snapshot

Artificial intelligence in the early stages of drug discovery

inferred← citeshttps://doi.org/10.1016/j.aichem.2024.100071openalex-snapshot

Artificial intelligence for drug repurposing against infectious diseases

inferred← citeshttps://doi.org/10.1016/j.artmed.2015.02.002openalex-snapshot

Machine learning in computational docking

inferred← citeshttps://doi.org/10.1016/j.asoc.2024.111875openalex-snapshot

Co-guided Dual-channel Graph Neural Networks for the prediction of compound–protein interaction

inferred← citeshttps://doi.org/10.1016/j.cmrp.2019.11.005openalex-snapshot

Current status and applications of Artificial Intelligence (AI) in medical field: An overview

inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2016.04.005openalex-snapshot

Machine learning optimization of cross docking accuracy

inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2018.09.014openalex-snapshot

A validation strategy for in silico generated aptamers

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110873openalex-snapshot

The use of knowledge graphs for drug repurposing: From classical machine learning algorithms to graph neural networks

inferred← citeshttps://doi.org/10.1016/j.drudis.2012.08.005openalex-snapshot

In silico drug repositioning – what we need to know

inferred← citeshttps://doi.org/10.1016/j.drudis.2018.08.007openalex-snapshot

Selection of protein conformations for structure-based polypharmacology studies

inferred← citeshttps://doi.org/10.1016/j.drudis.2022.103356openalex-snapshot

Concepts and applications of chemical fingerprint for hit and lead screening

inferred← citeshttps://doi.org/10.1016/j.ejmech.2013.09.041openalex-snapshot

Design, chemical synthesis of 3-(9H-fluoren-9-yl)pyrrolidine-2,5-dione derivatives and biological activity against enoyl-ACP reductase (InhA) and Mycobacterium tuberculosis

inferred← citeshttps://doi.org/10.1016/j.ejmech.2024.117164openalex-snapshot

Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine

inferred← citeshttps://doi.org/10.1016/j.engappai.2015.06.021openalex-snapshot

Comparative assessment of machine-learning scoring functions on PDBbind 2013

inferred← citeshttps://doi.org/10.1016/j.ijbiomac.2025.143751openalex-snapshot

AdptDilatedGCN: Protein-ligand binding affinity prediction based on multi-scale interaction fusion mechanism and dilated GCN

inferred← citeshttps://doi.org/10.1016/j.jare.2022.07.001openalex-snapshot

SCORCH: Improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation

inferred← citeshttps://doi.org/10.1016/j.jmgm.2017.12.002openalex-snapshot

PLHINT: A knowledge-driven computational approach based on the intermolecular H bond interactions at the protein-ligand interface from docking solutions

inferred← citeshttps://doi.org/10.1016/j.jmgm.2026.109282openalex-snapshot

RLBindDeep: A ResNet-LSTM based novel framework for protein–ligand binding affinity prediction

inferred← citeshttps://doi.org/10.1016/j.neunet.2025.107982openalex-snapshot

Dual-channel hierarchical interactive learning for the prediction of Protein-Ligand binding affinity

inferred← citeshttps://doi.org/10.1016/j.procbio.2022.09.026openalex-snapshot

Inhibition mechanism study for diallyl thiosulfinate (allicin) against crucial bacterial proteins through in silico molecular docking simulation

inferred← citeshttps://doi.org/10.1016/j.semcancer.2020.04.007openalex-snapshot

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs

inferred← citeshttps://doi.org/10.1016/j.semcancer.2021.07.012openalex-snapshot

Virtual screening of potential anticancer drugs based on microbial products

inferred← citeshttps://doi.org/10.1017/qrd.2022.11openalex-snapshot

Challenges and frontiers of computational modelling of biomolecular recognition

inferred← citeshttps://doi.org/10.1017/qrd.2022.11.pr1openalex-snapshot

Review: Challenges and Frontiers of Computational Modeling of Biomolecular Recognition — R0/PR1

inferred← citeshttps://doi.org/10.1017/qrd.2022.11.pr2openalex-snapshot

Review: Challenges and Frontiers of Computational Modeling of Biomolecular Recognition — R0/PR2

inferred← citeshttps://doi.org/10.1017/qrd.2022.11.pr3openalex-snapshot

Recommendation: Challenges and Frontiers of Computational Modeling of Biomolecular Recognition — R0/PR3

inferred← citeshttps://doi.org/10.1017/qrd.2022.11.pr4openalex-snapshot

Recommendation: Challenges and Frontiers of Computational Modeling of Biomolecular Recognition — R1/PR4

inferred← citeshttps://doi.org/10.1021/acs.chemrev.8b00728openalex-snapshot

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

inferred← citeshttps://doi.org/10.1021/acs.jcim.1c01078openalex-snapshot

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy

inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01841openalex-snapshot

Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction

inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00339openalex-snapshot

Predicting Binding Poses and Affinities in the CSAR 2013–2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential

inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01142openalex-snapshot

HMBVIP: A Novel Hierarchical Multi-Bio-View Intelligent Prediction Networks for Drug–Target Interaction Prediction

inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01276openalex-snapshot

Retrospective Benchmarking and Novel Shape-Pharmacophore Based Implementation of the MORLD Method for the Autonomous Optimization of 3-Aroyl-1,4-diarylpyrroles (ARDAP)

inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00355openalex-snapshot

Boosting Docking-Based Virtual Screening with Deep Learning

inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00226openalex-snapshot

Rigidity Strengthening: A Mechanism for Protein–Ligand Binding

inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00673openalex-snapshot

Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning

inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00334openalex-snapshot

AGL-Score: Algebraic Graph Learning Score for Protein–Ligand Binding Scoring, Ranking, Docking, and Screening

inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00645openalex-snapshot

Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions

inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5b02041openalex-snapshot

Delineation of Polypharmacology across the Human Structural Kinome Using a Functional Site Interaction Fingerprint Approach

inferred← citeshttps://doi.org/10.1021/acsomega.1c06976openalex-snapshot

Collaborative Approach between Explainable Artificial Intelligence and Simplified Chemical Interactions to Explore Active Ligands for Cyclin-Dependent Kinase 2

inferred← citeshttps://doi.org/10.1021/ci200057eopenalex-snapshot

Comments on “Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets”: Significance for the Validation of Scoring Functions

inferred← citeshttps://doi.org/10.1021/ci200562popenalex-snapshot

Application of Support Vector Machine to Three-Dimensional Shape-Based Virtual Screening Using Comprehensive Three-Dimensional Molecular Shape Overlay with Known Inhibitors

inferred← citeshttps://doi.org/10.1021/ci300458gopenalex-snapshot

Prediction of Ligand-Induced Structural Polymorphism of Receptor Interaction Sites Using Machine Learning

inferred← citeshttps://doi.org/10.1021/ci300493wopenalex-snapshot

ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein–Ligand Interactions

inferred← citeshttps://doi.org/10.1021/ci400321ropenalex-snapshot

Computation of Binding Energies Including Their Enthalpy and Entropy Components for Protein–Ligand Complexes Using Support Vector Machines

inferred← citeshttps://doi.org/10.1021/ci400510eopenalex-snapshot

Nonlinear Scoring Functions for Similarity-Based Ligand Docking and Binding Affinity Prediction

inferred← citeshttps://doi.org/10.1021/ci500091ropenalex-snapshot

Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?

inferred← citeshttps://doi.org/10.1021/ci500564bopenalex-snapshot

Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods—A Case Study of Serotonin Receptors 5-HT6 and 5-HT7

inferred← citeshttps://doi.org/10.1021/ci500672vopenalex-snapshot

A Virtual Screen Discovers Novel, Fragment-Sized Inhibitors ofMycobacterium tuberculosisInhA

inferred← citeshttps://doi.org/10.1038/npjsba.2016.23openalex-snapshot

Molecular mechanisms involved in the side effects of fatty acid amide hydrolase inhibitors: a structural phenomics approach to proteome-wide cellular off-target deconvolution and disease association

inferred← citeshttps://doi.org/10.1038/s41401-025-01580-0openalex-snapshot

Contrastive learning-based drug screening model for GluN1/GluN3A inhibitors

inferred← citeshttps://doi.org/10.1038/s42256-023-00715-4openalex-snapshot

Evaluating protein binding interfaces with transformer networks

inferred← citeshttps://doi.org/10.1038/srep40053openalex-snapshot

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme

inferred← citeshttps://doi.org/10.1039/9781849735377-00195openalex-snapshot

Binding Free Energy Calculation and Scoring in Small-Molecule Docking

inferred← citeshttps://doi.org/10.1039/c2cs35357aopenalex-snapshot

Drug repositioning by structure-based virtual screening

inferred← citeshttps://doi.org/10.1039/c3cp44697bopenalex-snapshot

A rapid identification of hit molecules for target proteins via physico-chemical descriptors

inferred← citeshttps://doi.org/10.1039/c5mb00118hopenalex-snapshot

Predicting the binding modes and sites of metabolism of xenobiotics

inferred← citeshttps://doi.org/10.1039/c5ra10729fopenalex-snapshot

Multivariate statistical analysis methods in QSAR

inferred← citeshttps://doi.org/10.1039/c9cs00786eopenalex-snapshot

Machine learning the ropes: principles, applications and directions in synthetic chemistry

inferred← citeshttps://doi.org/10.1039/c9ra01007fopenalex-snapshot

Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson's disease

inferred← citeshttps://doi.org/10.1039/c9ra09211kopenalex-snapshot

Improved method of structure-based virtual screening based on ensemble learning

inferred← citeshttps://doi.org/10.1039/d3dd00110eopenalex-snapshot

Machine learning-augmented docking. 1. CYP inhibition prediction

inferred← citeshttps://doi.org/10.1063/5.0060367openalex-snapshot

What the foundations of quantum computer science teach us about chemistry

inferred← citeshttps://doi.org/10.1063/5.0125206openalex-snapshot

Reinforcement learning: A novel approach towards drug discovery

inferred← citeshttps://doi.org/10.1080/07391102.2021.1905559openalex-snapshot

Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors

inferred← citeshttps://doi.org/10.1089/wound.2012.0422openalex-snapshot

Translational Bioinformatics Approaches to Drug Development

inferred← citeshttps://doi.org/10.1093/bfgp/elab031openalex-snapshot

Current status and future prospects of drug–target interaction prediction

inferred← citeshttps://doi.org/10.1093/bib/bbaa410openalex-snapshot

Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?

inferred← citeshttps://doi.org/10.1093/bib/bbab054openalex-snapshot

Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction

inferred← citeshttps://doi.org/10.1093/bib/bbac626openalex-snapshot

Reducing false positive rate of docking-based virtual screening by active learning

inferred← citeshttps://doi.org/10.1093/bib/bbad451openalex-snapshot

Multi-task bioassay pre-training for protein-ligand binding affinity prediction

inferred← citeshttps://doi.org/10.1093/bib/bbae333openalex-snapshot

GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction

inferred← citeshttps://doi.org/10.1093/bib/bbaf491openalex-snapshot

A survey on deep learning for drug-target binding prediction: models, benchmarks, evaluation, and case studies

inferred← citeshttps://doi.org/10.1093/bioadv/vbad072openalex-snapshot

GDockScore: a graph-based protein–protein docking scoring function

inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa921openalex-snapshot

GraphDTA: predicting drug–target binding affinity with graph neural networks

inferred← citeshttps://doi.org/10.1093/bioinformatics/btag001openalex-snapshot

A hybrid unsupervised methodology on artificial intelligence filtering for automatically processing cellular DNA-encoded library (DEL) datasets

inferred← citeshttps://doi.org/10.1093/bioinformatics/btw443openalex-snapshot

PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation

inferred← citeshttps://doi.org/10.1093/bioinformatics/bty374openalex-snapshot

Development and evaluation of a deep learning model for protein–ligand binding affinity prediction

inferred← citeshttps://doi.org/10.1093/database/baw083openalex-snapshot

‘RE:fine drugs’: an interactive dashboard to access drug repurposing opportunities

inferred← citeshttps://doi.org/10.1098/rsif.2012.0569openalex-snapshot

Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification

inferred← citeshttps://doi.org/10.1101/2022.11.18.515243openalex-snapshot

Network Based Identification of Holistic Drug Target for Parkinson Disease and Deep Learning assisted Drug Repurposing

inferred← citeshttps://doi.org/10.1101/2023.01.03.522623openalex-snapshot

PIsToN: Evaluating Protein Binding Interfaces with Transformer Networks

inferred← citeshttps://doi.org/10.1101/2023.03.14.532563openalex-snapshot

LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions

inferred← citeshttps://doi.org/10.1101/331975openalex-snapshot

Prioritizing Small Molecule as Candidates for Drug Repositioning using Machine Learning

inferred← citeshttps://doi.org/10.1101/684662openalex-snapshot

GraphDTA: Predicting drug–target binding affinity with graph neural networks

inferred← citeshttps://doi.org/10.1109/access.2025.3613332openalex-snapshot

ACSMPred-FRL: Accelerating the Identification of Anticancer Small Molecules Using Feature Representation Learning

inferred← citeshttps://doi.org/10.1109/access.2026.3670441openalex-snapshot

Accelerating Pharmaceutical Innovation Through AI: A Systematic Review of Machine Learning and Deep Learning Technologies for Drug-Drug Interaction Prediction

inferred← citeshttps://doi.org/10.1109/bibm66473.2025.11356513openalex-snapshot

AdaMM: An Adaptive Multimodal Model with Learnable Weights for Protein-Ligand Affinity Prediction

inferred← citeshttps://doi.org/10.1109/cibcb49929.2021.9562940openalex-snapshot

Random Forest to Predict Eucalyptus as a Potential Herb in Preventing Covid19

inferred← citeshttps://doi.org/10.1109/iementech65115.2025.10959578openalex-snapshot

CGDeepAff: Deep Learning-Based Approach for Protein-Ligand Binding Affinity Estimation Using CNN-GRU

inferred← citeshttps://doi.org/10.1109/jac-ecc61002.2023.10479635openalex-snapshot

Deep Learning is Competing with Random Forest in Computational Docking

inferred← citeshttps://doi.org/10.1109/tcbb.2022.3204188openalex-snapshot

Predicting Drug-Target Interactions Via Dual-Stream Graph Neural Network

inferred← citeshttps://doi.org/10.1109/tcbbio.2025.3563504openalex-snapshot

PGDTA: Predicting Drug-Target Affinity Using Three-Dimensional Structure of Protein Pocket and Graph Neural Network

inferred← citeshttps://doi.org/10.1109/tkde.2023.3314502openalex-snapshot

GIaNt: Protein-Ligand Binding Affinity Prediction via Geometry-Aware Interactive Graph Neural Network

inferred← citeshttps://doi.org/10.1134/s1990793126700338openalex-snapshot

Structural and Electronic Properties of Synthesized 1,2,3-Triazole Derivative: A DFT Study with Molecular Docking and Antitumor Evaluation

inferred← citeshttps://doi.org/10.1145/3447548.3467311openalex-snapshot

Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

inferred← citeshttps://doi.org/10.1155/2019/8427042openalex-snapshot

Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery

inferred← citeshttps://doi.org/10.11603/2312-0967.2024.3.14868openalex-snapshot

ВИКОРИСТАННЯ МЕТОДІВ ДОСЛІДЖЕНЬ IN SILICO ДЛЯ ПРОГНОЗУВАННЯ ФАРМАКОКІНЕТИЧНИХ ВЛАСТИВОСТЕЙ ТА ПОШУКУ БІОЛОГІЧНО АКТИВНИХ РЕЧОВИН

inferred← citeshttps://doi.org/10.1177/1535370221993422openalex-snapshot

Trends in application of advancing computational approaches in GPCR ligand discovery

inferred← citeshttps://doi.org/10.1186/1471-2105-13-s15-s6openalex-snapshot

Investigating drug repositioning opportunities in FDA drug labels through topic modeling

inferred← citeshttps://doi.org/10.1186/1750-1172-7-39openalex-snapshot

A generalizable pre-clinical research approach for orphan disease therapy

inferred← citeshttps://doi.org/10.1186/1755-8794-8-s4-s4openalex-snapshot

Multiple target drug cocktail design for attacking the core network markers of four cancers using ligand-based and structure-based virtual screening methods

inferred← citeshttps://doi.org/10.1186/s12859-016-0941-9openalex-snapshot

Drug repurposing to target Ebola virus replication and virulence using structural systems pharmacology

inferred← citeshttps://doi.org/10.1186/s12859-023-05503-wopenalex-snapshot

CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity

inferred← citeshttps://doi.org/10.1186/s12859-024-05991-4openalex-snapshot

A comprehensive survey of scoring functions for protein docking models

inferred← citeshttps://doi.org/10.1186/s12918-018-0658-7openalex-snapshot

Computational drug repositioning using meta-path-based semantic network analysis

inferred← citeshttps://doi.org/10.1186/s12936-018-2294-5openalex-snapshot

A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria

inferred← citeshttps://doi.org/10.1186/s13015-015-0034-8openalex-snapshot

MoDock: A multi-objective strategy improves the accuracy for molecular docking

inferred← citeshttps://doi.org/10.1186/s13321-017-0215-1openalex-snapshot

ChemSAR: an online pipelining platform for molecular SAR modeling

inferred← citeshttps://doi.org/10.1186/s13321-025-00955-zopenalex-snapshot

The algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity prediction

inferred← citeshttps://doi.org/10.1208/s12248-012-9322-0openalex-snapshot

Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review

inferred← citeshttps://doi.org/10.1248/cpb.c19-00443openalex-snapshot

Significance of Data Selection in Deep Learning for Reliable Binding Mode Prediction of Ligands in the Active Site of CYP3A4

inferred← citeshttps://doi.org/10.13023/etd.2018.137openalex-snapshot

Scalable Feature Selection and Extraction with Applications in Kinase Polypharmacology

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1005690openalex-snapshot

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1011301openalex-snapshot

Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks

inferred← citeshttps://doi.org/10.1371/journal.pone.0063240openalex-snapshot

Enhancing Hit Identification in Mycobacterium tuberculosis Drug Discovery Using Validated Dual-Event Bayesian Models

inferred← citeshttps://doi.org/10.1371/journal.pone.0220113openalex-snapshot

Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening

inferred← citeshttps://doi.org/10.1504/ijcbdd.2018.10015802openalex-snapshot

Comparative analysis of machine learning based QSAR models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of mycobacterium tuberculosis

inferred← citeshttps://doi.org/10.1515/hsz-2021-0119openalex-snapshot

Investigation of the structure of regulatory proteins interacting with glycosaminoglycans by combining NMR spectroscopy and molecular modeling – the beginning of a wonderful friendship

inferred← citeshttps://doi.org/10.1517/17460441.2016.1146250openalex-snapshot

Use of machine learning approaches for novel drug discovery

inferred← citeshttps://doi.org/10.1609/aaai.v38i11.29162openalex-snapshot

Binding-Adaptive Diffusion Models for Structure-Based Drug Design

inferred← citeshttps://doi.org/10.17537/2022.17.188openalex-snapshot

Development of a Deep Learning Generative Neural Network for Computer-Aided Design of Potential SARS-Cov-2 Inhibitors

inferred← citeshttps://doi.org/10.2147/aabc.s30881openalex-snapshot

Contact-based ligand-clustering approach for the identification of active compounds in virtual screening

inferred← citeshttps://doi.org/10.2174/0113892010298545241108062449openalex-snapshot

Advances in Molecular Docking Techniques for Targeting Protein Misfolding in Neurodegenerative Diseases

inferred← citeshttps://doi.org/10.2174/0115680266331755241008061915openalex-snapshot

ML-based Models as a Strategy to Discover Novel Antiepileptic Drugs Targeting Sodium Receptor Channel

inferred← citeshttps://doi.org/10.2174/0115680266339394250225112747openalex-snapshot

The Impact and Role of Artificial Intelligence (AI) in Healthcare: Systematic Review

inferred← citeshttps://doi.org/10.2174/0118741045455658260423122147openalex-snapshot

ROCK2 Inhibition Underlying the Anticancer Effects of Dobutamine: A Novel Proposed Mechanism

inferred← citeshttps://doi.org/10.2174/0929867324666170712115411openalex-snapshot

Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

inferred← citeshttps://doi.org/10.2174/0929867326666181203125229openalex-snapshot

Structural Basis for Inhibition of Enoyl-[Acyl Carrier Protein] Reductase (InhA) from Mycobacterium tuberculosis

inferred← citeshttps://doi.org/10.2174/1386207325666211207153943openalex-snapshot

Artificial Intelligence (AI) in Drugs and Pharmaceuticals

inferred← citeshttps://doi.org/10.2174/1573409914666181018141602openalex-snapshot

Virtual Screening Meets Deep Learning

inferred← citeshttps://doi.org/10.26434/chemrxiv-2022-gk3n6openalex-snapshot

CarbonAI, A Non-Docking Deep learning based small molecule virtual screening platform

inferred← citeshttps://doi.org/10.26434/chemrxiv-2022-gk3n6-v2openalex-snapshot

CarbonAI, A Non-Docking Deep learning based small molecule virtual screening platform

inferred← citeshttps://doi.org/10.31080/asmi.2024.07.1390openalex-snapshot

Harnessing Artificial Intelligence for the Design and Development of Novel Effective Medicines

inferred← citeshttps://doi.org/10.3389/fchem.2021.614073openalex-snapshot

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

inferred← citeshttps://doi.org/10.3389/fnins.2016.00265openalex-snapshot

Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

inferred← citeshttps://doi.org/10.3389/fphar.2022.974849openalex-snapshot

Drug repositioning: A bibliometric analysis

inferred← citeshttps://doi.org/10.3390/ijgi9020064openalex-snapshot

Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests

showing the first 200 of 225 · page through all 225

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.