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LigBuilder 2: A Practical de Novo Drug Design Approach

https://doi.org/10.1021/ci100350u
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What this work says

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

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Darstellung der Ester
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Estimating protein–ligand binding free energy: Atomic solvation parameters for partition coefficient and solvation free energy calculation
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PRO_LIGAND: An approach to de novo molecular design. 1. Application to the design of organic molecules
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The computer program LUDI: A new method for the de novo design of enzyme inhibitors
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SPROUT: A program for structure generation
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LigBuilder: A Multi-Purpose Program for Structure-Based Drug Design
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SCORE: A New Empirical Method for Estimating the Binding Affinity of a Protein-Ligand Complex
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Chemical modification of the naphthoyl 3-position of JWH-015: In search of a fluorescent probe to the cannabinoid CB2 receptor
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A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases
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ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
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Pocket v.2: Further Developments on Receptor-Based Pharmacophore Modeling
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Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation
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RASSE: A New Method for Structure-Based Drug Design
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Multiple Highly Diverse Structures Complementary to Enzyme Binding Sites: Results of Extensive Application of a de Novo Design Method Incorporating Combinatorial Growth
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The PDBbind Database: Methodologies and Updates
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LEA3D: A Computer-Aided Ligand Design for Structure-Based Drug Design
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Discovery and Optimization of p38 Inhibitors via Computer-Assisted Drug Design
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Discovering Potent Small Molecule Inhibitors of Cyclophilin A Using de Novo Drug Design Approach
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CONCERTS: Dynamic Connection of Fragments as an Approach to de Novo Ligand Design
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De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks
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Computer-based de novo design of drug-like molecules
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Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites
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LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation.
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Design of Selective Inhibitors of Tyrosine Kinase 2

What points at this work

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

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Shape-Based Generative Modeling for de Novo Drug Design
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Allosteric Type and Pathways Are Governed by the Forces of Protein–Ligand Binding
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Crystal structure of the Frizzled 4 receptor in a ligand-free state
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Structural basis of ligand recognition and self-activation of orphan GPR52
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An open-source molecular builder and free energy preparation workflow
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Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently
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Chemometric design to explore pharmacophore features of BACE inhibitors for controlling Alzheimer's disease
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Molecular mechanism of 15-lipoxygenase allosteric activation and inhibition
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A novel allosteric inhibitor that prevents IKKβ activation
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Efficient multi-objective molecular optimization in a continuous latent space
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Structure-based de novo drug design using 3D deep generative models
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Modeling protein–ligand interactions for drug discovery in the era of deep learning
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Precision fragment addition: domain-specific DeepFrag2 models for smarter lead optimization
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Microsomal prostaglandin E synthase-1 exhibits one-third-of-the-sites reactivity
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Towards structure-based protein drug design
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In silico development of new acetylcholinesterase inhibitors
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Identification of potential cruzain inhibitors using de novo design, molecular docking and dynamics simulations studies
inferred← citeshttps://doi.org/10.1080/14756366.2023.2290458openalex-snapshot
Identification of putative allosteric inhibitors of BCKDK via virtual screening and biological evaluation
inferred← citeshttps://doi.org/10.1080/17460441.2023.2234285openalex-snapshot
Linkers in fragment-based drug design: an overview of the literature
inferred← citeshttps://doi.org/10.1080/21655979.2022.2068755openalex-snapshot
Diosmetin attenuates oxidative stress-induced damage to lens epithelial cells via the mitogen-activated protein kinase (MAPK) pathway
inferred← citeshttps://doi.org/10.1093/bib/bbaa161openalex-snapshot
MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm
inferred← citeshttps://doi.org/10.1093/bib/bbae715openalex-snapshot
Directed evolution of antimicrobial peptides using multi-objective zeroth-order optimization
inferred← citeshttps://doi.org/10.1093/bioinformatics/bts637openalex-snapshot
Introducing Drugster: a comprehensive and fully integrated drug design, lead and structure optimization toolkit
inferred← citeshttps://doi.org/10.1093/bioinformatics/bty583openalex-snapshot
LigVoxel: inpainting binding pockets using 3D-convolutional neural networks
inferred← citeshttps://doi.org/10.1093/nar/gkad348openalex-snapshot
ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy
inferred← citeshttps://doi.org/10.1093/nar/gkw393openalex-snapshot
ACFIS: a web server for fragment-based drug discovery
inferred← citeshttps://doi.org/10.1101/2022.07.15.500218openalex-snapshot
Testing the Limits of SMILES-based De Novo Molecular Generation with Curriculum and Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/tevc.2024.3382145openalex-snapshot
A Survey on Evolutionary Computation-Based Drug Discovery
inferred← citeshttps://doi.org/10.1124/pr.112.007336openalex-snapshot
Computational Methods in Drug Discovery
inferred← citeshttps://doi.org/10.1186/1758-2946-6-28openalex-snapshot
iDrug: a web-accessible and interactive drug discovery and design platform
inferred← citeshttps://doi.org/10.1186/s13321-018-0287-6openalex-snapshot
Multi-objective de novo drug design with conditional graph generative model
inferred← citeshttps://doi.org/10.1186/s13321-020-00429-4openalex-snapshot
AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization
inferred← citeshttps://doi.org/10.1186/s13321-022-00582-yopenalex-snapshot
LEADD: Lamarckian evolutionary algorithm for de novo drug design
inferred← citeshttps://doi.org/10.1208/s12248-018-0216-7openalex-snapshot
Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications
inferred← citeshttps://doi.org/10.1360/ssc-2025-0118openalex-snapshot
Method development for potential drug target identification and drug discovery
inferred← citeshttps://doi.org/10.1371/journal.pone.0072146openalex-snapshot
Comparative Analyses of Lipoprotein Lipase, Hepatic Lipase, and Endothelial Lipase, and Their Binding Properties with Known Inhibitors
inferred← citeshttps://doi.org/10.1371/journal.pone.0134691openalex-snapshot
Computational Structure-Based De Novo Design of Hypothetical Inhibitors against the Anti- Inflammatory Target COX-2
inferred← citeshttps://doi.org/10.14806/ej.25.0.928openalex-snapshot
Dark Suite; a comprehensive toolbox for computer-aided drug design
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2611000/v1openalex-snapshot
Artificial Intelligence Uncovers Evolutionarily Conserved Intracellular Allosteric Modulators of GPCR-Gα Interface
inferred← citeshttps://doi.org/10.2147/jrlcr.s46845openalex-snapshot
Structure-based three-dimensional pharmacophores as an alternative to traditional methodologies
inferred← citeshttps://doi.org/10.2174/0113816128308066240529121148openalex-snapshot
Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics
inferred← citeshttps://doi.org/10.2174/092986712803530467openalex-snapshot
Fragment Based Drug Design: From Experimental to Computational Approaches
inferred← citeshttps://doi.org/10.2174/1568026611313100005openalex-snapshot
Open Source Software and Web Services for Designing Therapeutic Molecules
inferred← citeshttps://doi.org/10.2174/1568026614666140929115506openalex-snapshot
Revisiting De Novo Drug Design: Receptor Based Pharmacophore Screening
inferred← citeshttps://doi.org/10.2174/1570180816666190703152659openalex-snapshot
Lead Molecules as Novel Aromatase Inhibitors: In Silico De Novo Designing and Binding Affinity Studies
inferred← citeshttps://doi.org/10.2174/1573409915666191015110734openalex-snapshot
Proposition of Potential GSK-3β Inhibitors for the Treatment of Alzheimer’s Disease: A Molecular Modeling Study
inferred← citeshttps://doi.org/10.2174/1877946812666220919105403openalex-snapshot
In Silico Design of Molecular Analogues of 2-Butyl-5- pentylbenzene-1,3-diol (Stemphol) as Drug Prototypes for Treatment of Chemical Dependents of Cannabis Sativa
inferred← citeshttps://doi.org/10.2174/9789815136807123010008openalex-snapshot
Structural Bioinformatics and Artificial Intelligence Approaches in De Novo Drug Design
inferred← citeshttps://doi.org/10.2174/9789815179033124070003openalex-snapshot
In Silico Tools to Leverage Rational Drug Design and Development in LMICs
inferred← citeshttps://doi.org/10.2174/9789815179934123010005openalex-snapshot
Pharmacophore Mapping: An Important Tool in Modern Drug Design and Discovery
inferred← citeshttps://doi.org/10.2174/9789815179934123010008openalex-snapshot
FBDD & De Novo Drug Design
inferred← citeshttps://doi.org/10.21746/ijbio.2013.09.0018openalex-snapshot
RNA POLYMERASE INHIBITORS AGAINST PROTEASE NS3 HELICASE (DENGUE VIRUS) AS WELL AS DERIVED NOVEL SCAFFOLD AND VALIDATED BINDING AFFINITY FOLLOWED BY MOLECULAR PROPERTIES
inferred← citeshttps://doi.org/10.2751/jcac.16.15openalex-snapshot
A Mini-review on Chemoinformatics Approaches for Drug Discovery
inferred← citeshttps://doi.org/10.33263/briac103openalex-snapshot
inferred← citeshttps://doi.org/10.33263/briac103.338347openalex-snapshot
Screening of druggable conformers of α-synuclein using molecular dynamics simulation
inferred← citeshttps://doi.org/10.3389/fchem.2020.00093openalex-snapshot
In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery
inferred← citeshttps://doi.org/10.3389/fchem.2020.00142openalex-snapshot
LigBuilder V3: A Multi-Target de novo Drug Design Approach
inferred← citeshttps://doi.org/10.3389/fchem.2023.1288626openalex-snapshot
Multi-and many-objective optimization: present and future in de novo drug design
inferred← citeshttps://doi.org/10.3389/fphar.2018.01120openalex-snapshot
Activation of Glutathione Peroxidase 4 as a Novel Anti-inflammatory Strategy
inferred← citeshttps://doi.org/10.3389/fphar.2018.01463openalex-snapshot
An Improved Receptor-Based Pharmacophore Generation Algorithm Guided by Atomic Chemical Characteristics and Hybridization Types
inferred← citeshttps://doi.org/10.3390/bioengineering12040363openalex-snapshot
Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research
inferred← citeshttps://doi.org/10.3390/biom14070775openalex-snapshot
De Novo Design of Inhibitors of DNA Methyltransferase 1: A Critical Comparison of Ligand- and Structure-Based Approaches
inferred← citeshttps://doi.org/10.3390/cancers16213607openalex-snapshot
Computer-Aided Identification and Design of Ligands for Multi-Targeting Inhibition of a Molecular Acute Myeloid Leukemia Network
inferred← citeshttps://doi.org/10.3390/genes14122227openalex-snapshot
Dual Targeting of DNA Damage Response Proteins Implicated in Cancer Radioresistance
inferred← citeshttps://doi.org/10.3390/ijms231911265openalex-snapshot
Discovery of the Cryptic Sites of SARS-CoV-2 Papain-like Protease and Analysis of Its Druggability
inferred← citeshttps://doi.org/10.3390/molecules26175262openalex-snapshot
Identification of 3-((1-(Benzyl(2-hydroxy-2-phenylethyl)amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic Acid as a Potential Inhibitor of Non-Nucleosidase Reverse Transcriptase Inhibitors through InSilico Ligand- and Structure-Based Approaches
inferred← citeshttps://doi.org/10.3390/molecules27165241openalex-snapshot
Identification and Inhibition of the Druggable Allosteric Site of SARS-CoV-2 NSP10/NSP16 Methyltransferase through Computational Approaches
inferred← citeshttps://doi.org/10.3390/molecules27227715openalex-snapshot
Integration of Two-Dimensional Liquid Chromatography-Mass Spectrometry and Molecular Docking to Characterize and Predict Polar Active Compounds in Curcuma kwangsiensis
inferred← citeshttps://doi.org/10.3390/ph19020308openalex-snapshot
Frags2Drugs: A Novel In Silico Fragment-Based Approach to the Discovery of Kinase Inhibitors
inferred← citeshttps://doi.org/10.35516/jjps.v19i2.3563openalex-snapshot
6- Bajakah Plant Compounds as Potential Therapy for HER2-Positive Breast Cancer
inferred← citeshttps://doi.org/10.3762/bjoc.12.267openalex-snapshot
Computational methods in drug discovery
inferred← citeshttps://doi.org/10.3987/com-22-s(r)21openalex-snapshot
Oxidative Fragmentation of Cytisine as an Entry to the Bis(piperidine) Scaffold of Virgidivarine
inferred← citeshttps://doi.org/10.4018/978-1-5225-0362-0.ch001openalex-snapshot
Role of Molecular Docking in Computer-Aided Drug Design and Development
inferred← citeshttps://doi.org/10.4018/978-1-5225-1762-7.ch026openalex-snapshot
Role of Molecular Docking in Computer-Aided Drug Design and Development
inferred← citeshttps://doi.org/10.4018/ijsbbt.2013100101openalex-snapshot
Computer-Aided Drug Design and Biological Evaluation of Novel Anti-Greek Goat Encephalitis Agents
inferred← citeshttps://doi.org/10.4155/ebo.13.335openalex-snapshot
Computational fragment-based drug design
inferred← citeshttps://doi.org/10.4155/fmc-2018-0358openalex-snapshot
Deep Learning for Molecular Generation
inferred← citeshttps://doi.org/10.48550/arxiv.1801.07299openalex-snapshot
Multi-Objective De Novo Drug Design with Conditional Graph Generative Model
inferred← citeshttps://doi.org/10.48550/arxiv.2104.08474openalex-snapshot
Learning to design drug-like molecules in three-dimensional space using deep generative models
inferred← citeshttps://doi.org/10.6084/m9.figshare.18482568openalex-snapshot
Additional file 1 of LEADD: Lamarckian evolutionary algorithm for de novo drug design
inferred← citeshttps://doi.org/10.63028/10067/2051650151162165141openalex-snapshot
Computational design of synthesizable molecules by imitating reference chemistry
inferred← citeshttps://doi.org/10.70146/msv01i02.001openalex-snapshot
Assessing the Role of Artificial Intelligence in Cancer Treatment of Targeted Drug Delivery
inferred← citeshttps://doi.org/10.70675/1a4f06f0zdfa4z41efza87az70239c45c50copenalex-snapshot
Conception d'un logiciel pour la recherche de nouvelles molécules bioactives
inferred← citeshttps://doi.org/10.70675/c04eeb3az351fz48ddza731z6ad3d5044e23openalex-snapshot
Développement d'outils de chémoinformatique pour l'identification d'inhibiteurs de protéines kinases à partir de fragments
inferred← citeshttps://doi.org/10.7150/ijbs.41051openalex-snapshot
The Role of D-3-Phosphoglycerate Dehydrogenase in Cancer
inferred← citeshttps://doi.org/10.7554/elife.106397openalex-snapshot
Deep learning reveals endogenous sterols as allosteric modulators of the GPCR–Gα interface
inferred← citeshttps://doi.org/10.7554/elife.106397.1openalex-snapshot
Deep Learning Reveals Endogenous Sterols as Allosteric Modulators of the GPCR-Gα Interface
inferred← citeshttps://doi.org/10.7554/elife.106397.2openalex-snapshot
Deep Learning Reveals Endogenous Sterols as Allosteric Modulators of the GPCR-Gα Interface
inferred← citeshttps://doi.org/10.7554/elife.106397.3openalex-snapshot
Deep learning reveals endogenous sterols as allosteric modulators of the GPCR–Gα interface
inferred← citeshttps://doi.org/10.7717/peerj.725openalex-snapshot
DrugOn: a fully integrated pharmacophore modeling and structure optimization toolkit
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