Table 3 The Most Cited AI Articles in the Field of Drug Discovery from 1990 to 2023 Distribution (Bibliometrix & R software, 2023)
Paper | Title | Year | Magazine | DOI | Total Citations |
|---|---|---|---|---|---|
LECUN Y and Others | Deep learning | 2015 | NATURE | 10.1038/nature14539 | 20,779 |
CHING T and Others | Opportunities and obstacles for deep learning in biology and medicine | 2018 | J R SOC INTERFACE | 10.1098/rsif.2017.0387 | 924 |
VAMATHEVAN J and Others | Applications of machine learning in drug discovery and development | 2019 | NAT REV DRUG DISCOV | 10.1038/s41573-019-0024-5 | 907 |
BLAKEMORE DC and Others | Organic synthesis provides opportunities to transform drug discovery | 2018 | NAT CHEM | 10.1038/s41557-018-0021-z | 797 |
CHEN HM and Others | The rise of deep learning in drug discovery | 2018 | DRUG DISCOV TODAY | 10.1016/j.drudis.2018.01.039 | 792 |
KEARNES S and Others | Molecular graph convolutions: moving beyond fingerprints | 2016 | J COMPUT AID MOL DES | 10.1007/s10822-016-9938-8 | 789 |
PINZI L and Others | Molecular Docking: Shifting Paradigms in Drug Discovery | 2019 | INT J MOL SCI | 10.3390/ijms20184331 | 678 |
MA JS and Others | Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships | 2015 | J CHEM INF MODEL | 10.1021/ci500747n | 651 |
SCHNEIDER G and Others | Computer-based de novo design of drug-like molecules | 2005 | NAT REV DRUG DISCOV | 10.1038/nrd1799 | 618 |
HUANG SJ and Others | Applications of Support Vector Machine (SVM) Learning in Cancer Genomics | 2018 | CANCER GENOME PROTEOME | 10.21873/cgp.20063 | 604 |
GUPTA S and Others | In Silico Approach for Predicting Toxicity of Peptides and Proteins | 2013 | PLOS ONE | 10.1371/journal.pone.0073957 | 594 |
THOMFORD NE and Others | Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery | 2018 | INT J MOL SCI | 10.3390/ijms19061578 | 585 |
LO YC and Others | Machine learning in chemoinformatics and drug discovery | 2018 | DRUG DISCOV TODAY | 10.1016/j.drudis.2018.05.010 | 488 |
CAMACHO DM and Others | Next-Generation Machine Learning for Biological Networks | 2018 | CELL | 10.1016/j.cell.2018.05.015 | 470 |
BALLESTER PJ and Others | A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking | 2010 | BIOINFORMATICS | 10.1093/bioinformatics/btq112 | 464 |
ÖZTÜRK H and Others | DeepDTA: deep drug-target binding affinity prediction | 2018 | BIOINFORMATICS | 10.1093/bioinformatics/bty593 | 464 |
RAGOZA M and Others | Protein–Ligand Scoring with Convolutional Neural Networks | 2017 | J CHEM INF MODEL | 10.1021/acs.jcim.6b00740 | 428 |
JIMÉNEZ J and Others | Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks | 2018 | J CHEM INF MODEL | 10.1021/acs.jcim.7b00650 | 421 |
EKINS S and Others | In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling | 2007 | BRIT J PHARMACOL | 10.1038/sj.bjp.0707305 | 419 |
CHEN X and Others | Drug-target interaction prediction: databases, web servers and computational models | 2016 | BRIEF BIOINFORM | 10.1093/bib/bbv066 | 406 |