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Efficient and enhanced sampling of drug‐like chemical space for virtual screening and molecular design using modern machine learning methods

https://doi.org/10.1002/wcms.1637
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What this work says

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

inferredcites →https://doi.org/10.1002/jcc.21334openalex-snapshot
AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
inferredcites →https://doi.org/10.1002/qua.26870openalex-snapshot
Molecular representations for machine learning applications in chemistry
inferredcites →https://doi.org/10.1007/978-3-030-30493-5_77openalex-snapshot
Mol-CycleGAN - A Generative Model for Molecular Optimization
inferredcites →https://doi.org/10.1007/s12039-021-01995-2openalex-snapshot
Artificial intelligence: machine learning for chemical sciences
inferredcites →https://doi.org/10.1007/s12272-015-0640-5openalex-snapshot
Role of computer-aided drug design in modern drug discovery
inferredcites →https://doi.org/10.1016/j.jmr.2011.09.009openalex-snapshot
NMR studies of protein structure and dynamics
inferredcites →https://doi.org/10.1016/j.neucom.2021.04.039openalex-snapshot
Molecular generative Graph Neural Networks for Drug Discovery
inferredcites →https://doi.org/10.1016/j.physd.2019.132306openalex-snapshot
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network
inferredcites →https://doi.org/10.1016/j.sbi.2017.10.010openalex-snapshot
Protein structure-based drug design: from docking to molecular dynamics
inferredcites →https://doi.org/10.1016/s0959-440x(00)00105-6openalex-snapshot
Recent advances in structure-based rational drug design
inferredcites →https://doi.org/10.1021/acs.jcim.0c00075openalex-snapshot
RosENet: Improving Binding Affinity Prediction by Leveraging Molecular Mechanics Energies with an Ensemble of 3D Convolutional Neural Networks
inferredcites →https://doi.org/10.1021/acs.jcim.0c00174openalex-snapshot
The Synthesizability of Molecules Proposed by Generative Models
inferredcites →https://doi.org/10.1021/acs.jcim.0c00411openalex-snapshot
Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design
inferredcites →https://doi.org/10.1021/acs.jcim.0c00517openalex-snapshot
De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization
inferredcites →https://doi.org/10.1021/acs.jcim.0c00833openalex-snapshot
OptiMol : Optimization of Binding Affinities in Chemical Space for Drug Discovery
inferredcites →https://doi.org/10.1021/acs.jcim.1c00334openalex-snapshot
DeepBSP—a Machine Learning Method for Accurate Prediction of Protein–Ligand Docking Structures
inferredcites →https://doi.org/10.1021/acs.jcim.1c00600openalex-snapshot
MolGPT: Molecular Generation Using a Transformer-Decoder Model
inferredcites →https://doi.org/10.1021/acs.jcim.1c01341openalex-snapshot
MoleGuLAR: Molecule Generation Using Reinforcement Learning with Alternating Rewards
inferredcites →https://doi.org/10.1021/acs.jcim.7b00650openalex-snapshot
K DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
inferredcites →https://doi.org/10.1021/acs.jcim.8b00839openalex-snapshot
GuacaMol: Benchmarking Models for de Novo Molecular Design
inferredcites →https://doi.org/10.1021/acs.jcim.9b00694openalex-snapshot
Molecular Generative Model Based on an Adversarially Regularized Autoencoder
inferredcites →https://doi.org/10.1021/acs.jcim.9b00943openalex-snapshot
Bidirectional Molecule Generation with Recurrent Neural Networks
inferredcites →https://doi.org/10.1021/acs.jctc.1c00810openalex-snapshot
Efficient Exploration of Chemical Space with Docking and Deep Learning
inferredcites →https://doi.org/10.1021/acs.molpharmaceut.7b00346openalex-snapshot
druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico
inferredcites →https://doi.org/10.1021/acs.molpharmaceut.9b00634openalex-snapshot
From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design
inferredcites →https://doi.org/10.1021/acscentsci.0c00229openalex-snapshot
Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery
inferredcites →https://doi.org/10.1021/acscentsci.1c00546openalex-snapshot
Evidential Deep Learning for Guided Molecular Property Prediction and Discovery
inferredcites →https://doi.org/10.1021/acscentsci.7b00512openalex-snapshot
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
inferredcites →https://doi.org/10.1021/acscentsci.7b00572openalex-snapshot
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
inferredcites →https://doi.org/10.1021/acsomega.9b01997openalex-snapshot
OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction
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/ci050372xopenalex-snapshot
sc-PDB: an Annotated Database of Druggable Binding Sites from the Protein Data Bank
inferredcites →https://doi.org/10.1021/jm048957qopenalex-snapshot
The PDBbind Database: Methodologies and Updates
inferredcites →https://doi.org/10.1038/s41570-022-00363-zopenalex-snapshot
CACHE (Critical Assessment of Computational Hit-finding Experiments): A public–private partnership benchmarking initiative to enable the development of computational methods for hit-finding
inferredcites →https://doi.org/10.1038/s41586-019-0917-9openalex-snapshot
Ultra-large library docking for discovering new chemotypes
inferredcites →https://doi.org/10.1038/s41586-020-2027-0openalex-snapshot
Virtual discovery of melatonin receptor ligands to modulate circadian rhythms
inferredcites →https://doi.org/10.1038/s41586-020-2117-zopenalex-snapshot
An open-source drug discovery platform enables ultra-large virtual screens
inferredcites →https://doi.org/10.1038/s41586-021-03819-2openalex-snapshot
Highly accurate protein structure prediction with AlphaFold
inferredcites →https://doi.org/10.1038/s41598-020-78537-2openalex-snapshot
Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors
inferredcites →https://doi.org/10.1038/s42256-020-0174-5openalex-snapshot
Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks
inferredcites →https://doi.org/10.1039/c7sc02664aopenalex-snapshot
MoleculeNet: a benchmark for molecular machine learning
inferredcites →https://doi.org/10.1039/c9sc01928fopenalex-snapshot
Efficient multi-objective molecular optimization in a continuous latent space
inferredcites →https://doi.org/10.1039/c9sc04026aopenalex-snapshot
Constrained Bayesian optimization for automatic chemical design using variational autoencoders
inferredcites →https://doi.org/10.1039/d0sc06805eopenalex-snapshot
Accelerating high-throughput virtual screening through molecular pool-based active learning
inferredcites →https://doi.org/10.1039/d1cc07035eopenalex-snapshot
Modern machine learning for tackling inverse problems in chemistry: molecular design to realization
inferredcites →https://doi.org/10.1039/d1sc02783bopenalex-snapshot
MEMES: Machine learning framework for Enhanced MolEcular Screening
inferredcites →https://doi.org/10.1039/d1sc04444copenalex-snapshot
Structure-based de novo drug design using 3D deep generative models
inferredcites →https://doi.org/10.1088/2632-2153/abcf91openalex-snapshot
Graph networks for molecular design
inferredcites →https://doi.org/10.1093/nar/28.1.235openalex-snapshot
The Protein Data Bank
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1126/sciadv.aap7885openalex-snapshot
Deep reinforcement learning for de novo drug design
inferredcites →https://doi.org/10.1126/science.abj8754openalex-snapshot
Accurate prediction of protein structures and interactions using a three-track neural network
inferredcites →https://doi.org/10.1145/3422622openalex-snapshot
Generative adversarial networks
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-snapshot
Long Short-Term Memory
inferredcites →https://doi.org/10.1177/1087057108319644openalex-snapshot
The Future of High-Throughput Screening
inferredcites →https://doi.org/10.1186/s12859-019-2932-0openalex-snapshot
ProteinNet: a standardized data set for machine learning of protein structure
inferredcites →https://doi.org/10.1186/s13321-018-0286-7openalex-snapshot
Molecular generative model based on conditional variational autoencoder for de novo molecular design
inferredcites →https://doi.org/10.1186/s13321-019-0397-9openalex-snapshot
A de novo molecular generation method using latent vector based generative adversarial network
inferredcites →https://doi.org/10.1186/s13321-020-00419-6openalex-snapshot
Multiobjective de novo drug design with recurrent neural networks and nondominated sorting
inferredcites →https://doi.org/10.1186/s13321-020-00454-3openalex-snapshot
DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach
inferredcites →https://doi.org/10.1186/s13321-021-00522-2openalex-snapshot
GNINA 1.0: molecular docking with deep learning
inferredcites →https://doi.org/10.1186/s13321-022-00599-3openalex-snapshot
Transformer-based molecular optimization beyond matched molecular pairs
inferredcites →https://doi.org/10.26434/chemrxiv.14348117.v1openalex-snapshot
State of the Art Iterative Docking with Logistic Regression and Morgan Fingerprints
inferredcites →https://doi.org/10.3389/fphar.2020.565644openalex-snapshot
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
inferredcites →https://doi.org/10.3390/molecules26237369openalex-snapshot
Virtual Screening with Gnina 1.0
inferredcites →https://doi.org/10.4230/lipics.giscience.2023.43openalex-snapshot
Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics
inferredcites →https://doi.org/10.48550/arxiv.1805.09076openalex-snapshot
Constrained Graph Variational Autoencoders for Molecule Design
inferredcites →https://doi.org/10.48550/arxiv.1806.02473openalex-snapshot
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

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

inferred← citeshttps://doi.org/10.1002/agt2.365openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11030-025-11372-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.drudis.2024.103992openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.drudis.2024.104133openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.hybadv.2024.100235openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.phrs.2025.107856openalex-snapshot
The role of nuclear receptors in metabolic homeostasis and disease: From molecular mechanisms to drug discovery
inferred← citeshttps://doi.org/10.1016/j.ymeth.2023.01.004openalex-snapshot
Explore drug-like space with deep generative models
inferred← citeshttps://doi.org/10.1016/j.ymeth.2023.12.009openalex-snapshot
Application scenario-oriented molecule generation platform developed for drug discovery
inferred← citeshttps://doi.org/10.1021/acs.chemrev.3c00189openalex-snapshot
Machine Learning Methods for Small Data Challenges in Molecular Science
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01371openalex-snapshot
Deep Learning with Geometry-Enhanced Molecular Representation for Augmentation of Large-Scale Docking-Based Virtual Screening
inferred← citeshttps://doi.org/10.1021/acsomega.3c03456openalex-snapshot
Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation
inferred← citeshttps://doi.org/10.1039/9781837070206-00386openalex-snapshot
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inferred← citeshttps://doi.org/10.1039/d4sc03921aopenalex-snapshot
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inferred← citeshttps://doi.org/10.1080/07391102.2024.2445169openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bib/bbae531openalex-snapshot
Deep reinforcement learning as an interaction agent to steer fragment-based 3D molecular generation for protein pockets
inferred← citeshttps://doi.org/10.1093/bib/bbag257openalex-snapshot
KSDiffusion: conditional diffusion for kinase-specific phosphorylation site prediction under data-limited and imbalanced regimes
inferred← citeshttps://doi.org/10.1126/sciadv.ady9955openalex-snapshot
Steering semi-flexible molecular diffusion model for structure-based drug design with reinforcement learning
inferred← citeshttps://doi.org/10.26434/chemrxiv-2023-mg07dopenalex-snapshot
PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications
inferred← citeshttps://doi.org/10.3389/fddsv.2025.1724392openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/pharmaceutics17050612openalex-snapshot
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inferred← citeshttps://doi.org/10.34133/csbj.0008openalex-snapshot
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