Research graph
References from Artificial intelligence-enabled tools used in drug designing. Local targets link to admitted publications; unresolved targets remain external evidence.
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
10.1038/s41592-024-02272-z · 2024 · External reference
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
10.1038/nbt.3300 · 2015 · External reference
Accurate prediction of protein structures and interactions using a three-track neural network
10.1126/science.abj8754 · 2021 · External reference
Unresolved reference
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Fueling the digital chemistry revolution with language models
10.2533/chimia.2023.484 · 2023 · External reference
Precise atom-to-atom mapping for organic reactions via human-in-the-loop machine learning
2024 · External reference
Robust deep learning–based protein sequence design using ProteinMPNN
10.1126/science.add2187 · 2022 · External reference
Unresolved reference
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PADME: a deep learning-based framework for drug-target interaction prediction
2018 · External reference
ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support
10.1093/nar/gkae236 · 2024 · External reference
Automatic chemical design using a data-driven continuous representation of molecules
10.1021/acscentsci.7b00572 · 2018 · External reference
Link-INVENT: generative linker design with reinforcement learning
10.1039/d2dd00115b · 2023 · External reference
Machine learning-driven bioavailability prediction in early-stage drug development: a KNIME-based computational workflow for digital health applications
10.1080/00498254.2025.2508804 · 2025 · External reference
Chemprop: a machine learning package for chemical property prediction
10.1021/acs.jcim.3c01250 · 2024 · External reference
DiffDock-Glide: a hybrid physics-based and data-driven approach to molecular docking
2025 · External reference
DeepPurpose: a deep learning library for drug-target interaction prediction
10.1093/bioinformatics/btaa1005 · 2020 · External reference
MolTrans: molecular interaction transformer for drug-target interaction prediction
10.1093/bioinformatics/btaa880 · 2021 · External reference
Chemistry42: an AI-driven platform for molecular design and optimization
10.1021/acs.jcim.2c01191 · 2023 · External reference
PandaOmics: an AI-driven platform for therapeutic target and biomarker discovery
10.1021/acs.jcim.3c01619 · 2024 · External reference
DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks
10.1093/bioinformatics/btz111 · 2019 · External reference
ABodyBuilder3: improved and scalable antibody structure predictions
10.1093/bioinformatics/btae576 · 2024 · External reference
DeepGraphMol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach
2020 · External reference
Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches
10.1007/s12257-020-0049-y · 2020 · External reference
Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation
10.1088/2632-2153/aba947 · 2020 · External reference
P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
10.1186/s13321-018-0285-8 · 2018 · External reference
DeepConv-DTI: prediction of drug-target interactions via deep learning with convolution on protein sequences
10.1371/journal.pcbi.1007129 · 2019 · External reference
Reinvent 4: modern AI–driven generative molecule design
10.1186/s13321-024-00812-5 · 2024 · External reference
Drug similarity integration through attentive multi-view graph auto-encoders
10.24963/ijcai.2018/483 · 2018 · External reference
DeepTox: toxicity prediction using deep learning
10.3389/fenvs.2015.00080 · 2016 · External reference
GNINA 1.3: the next increment in molecular docking with deep learning
10.1186/s13321-025-00973-x · 2025 · External reference
Artificial intelligence in small-molecule drug discovery: a critical review of methods, applications, and real-world outcomes
10.3390/ph18091271 · 2025 · External reference
DeepDTA: deep drug–target binding affinity prediction
10.1093/bioinformatics/bty593 · 2018 · External reference
WideDTA: prediction of drug-target binding affinity
2019 · External reference
Integrating synthetic accessibility with AI-based generative drug design
10.1186/s13321-023-00742-8 · 2023 · External reference
Hypervariable-locus melting typing (HLMT): a novel, fast and inexpensive sequencing-free approach to pathogen typing based on high resolution melting (HRM) analysis
2021 · External reference
pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures
10.1021/acs.jmedchem.5b00104 · 2015 · External reference
Unresolved reference
External reference
AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor
10.1039/d2sc05709c · 2023 · External reference
DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
10.1038/s41467-021-27396-0 · 2021 · External reference
DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations
10.1039/c9sc03414e · 2020 · External reference
Benchmarking AutoML frameworks for disease prediction using medical claims
10.1186/s13040-022-00300-2 · 2022 · External reference
AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application
10.1186/s13321-024-00860-x · 2024 · External reference
Explainable drug sensitivity prediction through cancer pathway enrichment
2021 · External reference
Synthesis, antitumor evaluation, molecular modeling and quantitative structure–activity relationship (Qsar) of novel 2-[(4-amino-6-n-substituted-1,3,5-triazin-2-yl)methylthio]-4-chloro-5-methyl-n-(1h-benzo[d]imidazol-2(3h)-ylidene)benzenesulfonamides
10.3390/ijms21082924 · 2020 · External reference
ASKCOS: open-source, data-driven synthesis planning
10.1021/acs.accounts.5c00155 · 2025 · External reference
Applications of machine learning in drug discovery and development
10.1038/s41573-019-0024-5 · 2019 · External reference
AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
10.1093/nar/gkab1061 · 2022 · External reference
Integrating artificial intelligence for drug discovery in the context of revolutionizing drug delivery
10.3390/life14020233 · 2024 · External reference
DeepCPI: a deep learning-based framework for large-scale in silico drug screening
10.1016/j.gpb.2019.04.003 · 2019 · External reference
De novo design of protein structure and function with RFdiffusion
10.1038/s41586-023-06415-8 · 2023 · External reference
Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
10.1039/c8sc04175j · 2019 · External reference
MoleculeNet: a benchmark for molecular machine learning
10.1039/c7sc02664a · 2018 · External reference
DeepDrug: a general graph‐based deep learning framework for drug‐drug interactions and drug‐target interactions prediction
10.15302/j-qb-022-0320 · 2023 · External reference
Protein–peptide docking with ESMFold language model
10.1021/acs.jctc.4c01585 · 2025 · External reference
DeepSite: bidirectional LSTM and CNN models for predicting DNA–protein binding
10.1007/s13042-019-00990-x · 2020 · External reference
TorchDrug: a powerful and flexible machine learning platform for drug discovery
2022 · External reference
Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches
10.1007/s12257-020-0049-y · ExternalCitation · doi-reference
DeepSite: bidirectional LSTM and CNN models for predicting DNA–protein binding
10.1007/s13042-019-00990-x · ExternalCitation · doi-reference
DeepCPI: a deep learning-based framework for large-scale in silico drug screening
10.1016/j.gpb.2019.04.003 · ExternalCitation · doi-reference
ASKCOS: open-source, data-driven synthesis planning
10.1021/acs.accounts.5c00155 · ExternalCitation · doi-reference
Chemistry42: an AI-driven platform for molecular design and optimization
10.1021/acs.jcim.2c01191 · ExternalCitation · doi-reference
Chemprop: a machine learning package for chemical property prediction
10.1021/acs.jcim.3c01250 · ExternalCitation · doi-reference
PandaOmics: an AI-driven platform for therapeutic target and biomarker discovery
10.1021/acs.jcim.3c01619 · ExternalCitation · doi-reference
Protein–peptide docking with ESMFold language model
10.1021/acs.jctc.4c01585 · ExternalCitation · doi-reference
pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures
10.1021/acs.jmedchem.5b00104 · ExternalCitation · doi-reference
Automatic chemical design using a data-driven continuous representation of molecules
10.1021/acscentsci.7b00572 · ExternalCitation · doi-reference
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
10.1038/nbt.3300 · ExternalCitation · doi-reference
DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
10.1038/s41467-021-27396-0 · ExternalCitation · doi-reference
Applications of machine learning in drug discovery and development
10.1038/s41573-019-0024-5 · ExternalCitation · doi-reference
De novo design of protein structure and function with RFdiffusion
10.1038/s41586-023-06415-8 · ExternalCitation · doi-reference
OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
10.1038/s41592-024-02272-z · ExternalCitation · doi-reference
MoleculeNet: a benchmark for molecular machine learning
10.1039/c7sc02664a · ExternalCitation · doi-reference
Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
10.1039/c8sc04175j · ExternalCitation · doi-reference
DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations
10.1039/c9sc03414e · ExternalCitation · doi-reference
Link-INVENT: generative linker design with reinforcement learning
10.1039/d2dd00115b · ExternalCitation · doi-reference
AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor
10.1039/d2sc05709c · ExternalCitation · doi-reference
Machine learning-driven bioavailability prediction in early-stage drug development: a KNIME-based computational workflow for digital health applications
10.1080/00498254.2025.2508804 · ExternalCitation · doi-reference
Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation
10.1088/2632-2153/aba947 · ExternalCitation · doi-reference
DeepPurpose: a deep learning library for drug-target interaction prediction
10.1093/bioinformatics/btaa1005 · ExternalCitation · doi-reference
MolTrans: molecular interaction transformer for drug-target interaction prediction
10.1093/bioinformatics/btaa880 · ExternalCitation · doi-reference
ABodyBuilder3: improved and scalable antibody structure predictions
10.1093/bioinformatics/btae576 · ExternalCitation · doi-reference
DeepDTA: deep drug–target binding affinity prediction
10.1093/bioinformatics/bty593 · ExternalCitation · doi-reference
DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks
10.1093/bioinformatics/btz111 · ExternalCitation · doi-reference
AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
10.1093/nar/gkab1061 · ExternalCitation · doi-reference
ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support
10.1093/nar/gkae236 · ExternalCitation · doi-reference
Accurate prediction of protein structures and interactions using a three-track neural network
10.1126/science.abj8754 · ExternalCitation · doi-reference
Robust deep learning–based protein sequence design using ProteinMPNN
10.1126/science.add2187 · ExternalCitation · doi-reference
Benchmarking AutoML frameworks for disease prediction using medical claims
10.1186/s13040-022-00300-2 · ExternalCitation · doi-reference
P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
10.1186/s13321-018-0285-8 · ExternalCitation · doi-reference
Integrating synthetic accessibility with AI-based generative drug design
10.1186/s13321-023-00742-8 · ExternalCitation · doi-reference
Reinvent 4: modern AI–driven generative molecule design
10.1186/s13321-024-00812-5 · ExternalCitation · doi-reference
AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application
10.1186/s13321-024-00860-x · ExternalCitation · doi-reference
GNINA 1.3: the next increment in molecular docking with deep learning
10.1186/s13321-025-00973-x · ExternalCitation · doi-reference
DeepConv-DTI: prediction of drug-target interactions via deep learning with convolution on protein sequences
10.1371/journal.pcbi.1007129 · ExternalCitation · doi-reference
DeepDrug: a general graph‐based deep learning framework for drug‐drug interactions and drug‐target interactions prediction
10.15302/j-qb-022-0320 · ExternalCitation · doi-reference
Drug similarity integration through attentive multi-view graph auto-encoders
10.24963/ijcai.2018/483 · ExternalCitation · doi-reference
Fueling the digital chemistry revolution with language models
10.2533/chimia.2023.484 · ExternalCitation · doi-reference
DeepTox: toxicity prediction using deep learning
10.3389/fenvs.2015.00080 · ExternalCitation · doi-reference
Synthesis, antitumor evaluation, molecular modeling and quantitative structure–activity relationship (Qsar) of novel 2-[(4-amino-6-n-substituted-1,3,5-triazin-2-yl)methylthio]-4-chloro-5-methyl-n-(1h-benzo[d]imidazol-2(3h)-ylidene)benzenesulfonamides
10.3390/ijms21082924 · ExternalCitation · doi-reference
Integrating artificial intelligence for drug discovery in the context of revolutionizing drug delivery
10.3390/life14020233 · ExternalCitation · doi-reference
Artificial intelligence in small-molecule drug discovery: a critical review of methods, applications, and real-world outcomes
10.3390/ph18091271 · ExternalCitation · doi-reference
