Artificial intelligence in chemistry and drug design

Can the pharmaceutical industry reduce attrition rates?

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Handbook of Molecular Descriptors

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PubChem 2019 update: improved access to chemical data

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Applications of machine learning in drug discovery and development

Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228

ZINC 15 – Ligand Discovery for Everyone

Sterling, T., & Irwin, J. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00559 · Cited by 3,224

MoleculeNet: a benchmark for molecular machine learning

Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106

A Deep Learning Approach to Antibiotic Discovery

Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463

ChEMBL: towards direct deposition of bioassay data

Mendez, D., Gaulton, A., Bento, A. P., Chambers, J., De Veij, M., Félix, E., Magariños, M. P., Mosquera, J. F., Mutowo, P., Nowotka, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1075 · Cited by 2,372

p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure

Hansch, C., & Fujita, T. (1964) · Journal of the American Chemical Society · doi:10.1021/ja01062a035 · Cited by 2,313

Inverse molecular design using machine learning: Generative models for matter engineering

Sánchez-Lengeling, B., & Aspuru‐Guzik, A. (2018) · Science · doi:10.1126/science.aat2663 · Cited by 2,177

Robust physical-world attacks on deep learning visual classification

Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., & Song, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00175 · Cited by 2,173

Analyzing Learned Molecular Representations for Property Prediction

Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00237 · Cited by 1,923

Planning chemical syntheses with deep neural networks and symbolic AI

Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.

Gómez‐Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera‐Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru‐Guzik, A. (2018) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.34134 · Cited by 1,813

The rise of deep learning in drug discovery

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770

Molecular graph convolutions: moving beyond fingerprints

Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537

Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

Ruddigkeit, L., van Deursen, R., Blum, L. C., & Reymond, J.-L. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300415d · Cited by 1,505

Molecular de-novo design through deep reinforcement learning

Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0235-x · Cited by 1,443

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

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Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500747n · Cited by 1,159

Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Samek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080

Junction Tree Variational Autoencoder for Molecular Graph GenerationPreprint

Jin, W., Barzilay, R., & Jaakkola, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04364 · Cited by 1,053

Unmasking Clever Hans predictors and assessing what machines really learn

Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K.-R. (2019) · Nature Communications · doi:10.1038/s41467-019-08987-4 · Cited by 1,047

Navigating chemical space for biology and medicine

Lipinski, C. A., & Hopkins, A. L. (2004) · Nature · doi:10.1038/nature03193 · Cited by 1,028

A Mathematical Contribution to Structure-Activity Studies

Free, S. M., & Wilson, J. W. (1964) · Journal of Medicinal Chemistry · doi:10.1021/jm00334a001 · Cited by 953

Untitled work

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Automating drug discovery

Schneider, G. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.232 · Cited by 910

GuacaMol: Benchmarking Models for de Novo Molecular Design

Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895

Rethinking drug design in the artificial intelligence era

Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894

Low Data Drug Discovery with One-Shot Learning

Altae-Tran, H., Ramsundar, B., Pappu, A. S., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.6b00367 · Cited by 799

Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning

Raschka, S. (2018) · arXiv · doi:10.48550/arxiv.1811.12808 · Cited by 735

The Chemical Space Project

Reymond, J.-L. (2015) · Accounts of Chemical Research · doi:10.1021/ar500432k · Cited by 720

Machine Learning in Computer-Aided Synthesis Planning

Coley, C. W., Green, W. H., & Jensen, K. F. (2018) · Accounts of Chemical Research · doi:10.1021/acs.accounts.8b00087 · Cited by 704

Deep learning for molecular design—a review of the state of the art

Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683

A graph-convolutional neural network model for the prediction of chemical reactivity

Coley, C. W., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., & Jensen, K. F. (2019) · Chemical Science · doi:10.1039/c8sc04228d · Cited by 679

DNA-encoded chemistry: enabling the deeper sampling of chemical space

Goodnow, R. A., Dumelin, C. E., & Keefe, A. D. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.213 · Cited by 621

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science · doi:10.1039/c8sc00148k · Cited by 584

Unmasking Clever Hans predictors and assessing what machines really learn

Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K. (2019) · RePEc: Research Papers in Economics · Cited by 580

Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships

Sheridan, R. P., Wang, W. M., Liaw, A., Ma, J., & Gifford, E. M. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00591 · Cited by 544

Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations

Winter, R., Montanari, F., Noé, F., & Clevert, D.-A. (2018) · Chemical Science · doi:10.1039/c8sc04175j · Cited by 530

Methods and Principles in Medicinal Chemistry

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How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)

Dearden, J. C., Cronin, M. T. D., & Kaiser, K. L. E. (2009) · SAR and QSAR in Environmental Research · doi:10.1080/10629360902949567 · Cited by 468

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

Méndez‐Lucio, O., Baillif, B., Clevert, D.-A., Rouquié, D., & Wichard, J. (2020) · Nature Communications · doi:10.1038/s41467-019-13807-w · Cited by 441

Using Machine Learning To Predict Suitable Conditions for Organic Reactions

Gao, H., Struble, T. J., Coley, C. W., Wang, Y., Green, W. H., & Jensen, K. F. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00357 · Cited by 431

A de novo molecular generation method using latent vector based generative adversarial network

Prykhodko, O., Johansson, S., Kotsias, P.-C., Arús‐Pous, J., Bjerrum, E. J., Engkvist, O., & Chen, H. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0397-9 · Cited by 415

Why deep-learning AIs are so easy to fool

Heaven, D. (2019) · Nature · doi:10.1038/d41586-019-03013-5 · Cited by 342

In Need of Bias Control: Evaluating Chemical Data for Machine Learning in Structure-Based Virtual Screening

Sieg, J., Flachsenberg, F., & Rarey, M. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00712 · Cited by 332

Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization

Wallach, I., & Heifets, A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00403 · Cited by 324

Synthetic organic chemistry driven by artificial intelligence

Almeida, A. F., Moreira, R., & Rodrigues, T. (2019) · Nature Reviews Chemistry · doi:10.1038/s41570-019-0124-0 · Cited by 318

Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR

Sheridan, R. P., Feuston, B. P., Maiorov, V. N., & Kearsley, S. K. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci049782w · Cited by 300

ImageNet: Constructing a large-scale image database

Fei-Fei, L., Deng, J., & Li, K. (2010) · Journal of Vision · doi:10.1167/9.8.1037 · Cited by 297

Active-learning strategies in computer-assisted drug discovery

Reker, D., & Schneider, G. (2014) · Drug Discovery Today · doi:10.1016/j.drudis.2014.12.004 · Cited by 275

Extraction of chemical structures and reactions from the literature

Lowe, D. M. (2012) · Apollo (University of Cambridge) · doi:10.17863/cam.16293 · Cited by 263

Is Multitask Deep Learning Practical for Pharma?

Ramsundar, B., Liu, B., Wu, Z., Verras, A., Tudor, M., Sheridan, R. P., & Pande, V. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00146 · Cited by 263

Assessing the impact of generative AI on medicinal chemistry

Walters, W. P., & Murcko, M. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0418-2 · Cited by 262

Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?

Varnek, A. A., & Baskin, I. I. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci200409x · Cited by 255

The next level in chemical space navigation: going far beyond enumerable compound libraries

Hoffmann, T., & Gastreich, M. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.02.013 · Cited by 253

Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships

Xu, Y., Ma, J., Liaw, A., Sheridan, R. P., & Svetnik, V. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00087 · Cited by 238

Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets

Wenzel, J., Matter, H., & Schmidt, F. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00785 · Cited by 230

New drugs cost US$2.6 billion to develop

Mullard, A. (2014) · Nature Reviews Drug Discovery · doi:10.1038/nrd4507 · Cited by 228

Comparison of the NCI Open Database with Seven Large Chemical Structural Databases

Voigt, J. H., Bienfait, B., Wang, S., & Nicklaus, M. C. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci000150t · Cited by 217

Virtual Chemical Libraries

Walters, W. P. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b01048 · Cited by 212

Data integration: challenges for drug discovery

Searls, D. B. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1608 · Cited by 204

Computational prediction of chemical reactions: current status and outlook

Engkvist, O., Norrby, P., Selmi, N., Lam, Y., Peng, Z., Sherer, E. C., Amberg, W., Erhard, T., & Smyth, L. A. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.02.014 · Cited by 189

Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)Preprint

Sánchez-Lengeling, B., Outeiral, C., Guimaraes, G. L., & Aspuru‐Guzik, A. (2017) · ChemRxiv · doi:10.26434/chemrxiv.5309668.v2 · Cited by 156

The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting SecretsPreprint

Carlini, N., Liu, C., Kos, J., Erlingsson, Ú., & Song, D. X. (2018) · arXiv (Cornell University) · Cited by 124

Cybernetics and forecasting techniques

Ivakhnenko, A., Lapa, V. G., & McDonough, R. N. (1967) · CERN Document Server (European Organization for Nuclear Research) · Cited by 119

Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks

Montanari, F., Kuhnke, L., ter Laak, A., & Clevert, D.-A. (2019) · Molecules · doi:10.3390/molecules25010044 · Cited by 115

In silico generation of novel, drug-like chemical matter using the LSTM neural networkPreprint

Ertl, P., Lewis, R. A., Martin, É., & Polyakov, V. R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.07449 · Cited by 93

Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?

Sheridan, R. P. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00825 · Cited by 84

Knowledge-Based Approach to de Novo Design Using Reaction Vectors

Patel, H., Bodkin, M. J., Chen, B., & Gillet, V. J. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800413m · Cited by 82

Exploring Tunable Hyperparameters for Deep Neural Networks with Industrial ADME Data Sets

Zhou, Y., Cahya, S., Combs, S. A., Nicolaou, C. A., Wang, J., Desai, P. V., & Shen, J. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00671 · Cited by 82

Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors

Reker, D., Schneider, P., & Schneider, G. (2016) · Chemical Science · doi:10.1039/c5sc04272k · Cited by 81

A General Method for Exploiting QSAR Models in Lead Optimization

Lewis, R. A. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049228d · Cited by 77

Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction

Robinson, M. C., Glen, R. C., & Lee, A. A. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00274-0 · Cited by 76

Imputation of Assay Bioactivity Data Using Deep Learning

Whitehead, T. M., Irwin, B., Hunt, P. A., Segall, M., & Conduit, G. J. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00768 · Cited by 71

Revealing cytotoxic substructures in molecules using deep learning

Webel, H. E., Kimber, T. B., Radetzki, S., Neuenschwander, M., Nazaré, M., & Volkamer, A. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00310-4 · Cited by 67

Adversarial Controls for Scientific Machine Learning

Chuang, K. V., & Keiser, M. J. (2018) · ACS Chemical Biology · doi:10.1021/acschembio.8b00881 · Cited by 66

Improving 2D Face Recognition via Discriminative Face Depth Estimation

Cui, J., Zhang, H., Han, H., Shan, S., & Chen, X. (2018) · International Conference on Biometrics · doi:10.1109/icb2018.2018.00031 · Cited by 64

BRADSHAW: a system for automated molecular design

Green, D. V. S., Pickett, S. D., Luscombe, C. N., Senger, S., Marcus, D., Meslamani, J. E., Brett, D., Powell, A., & Masson, J. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00234-8 · Cited by 62

Estimation of the applicability domain of kernel-based machine learning models for virtual screening

Fechner, N., Jahn, A., Hinselmann, G., & Zell, A. (2010) · Journal of Cheminformatics · doi:10.1186/1758-2946-2-2 · Cited by 54

Evaluation of data augmentation via synthetic images for improved breast mass detection on mammograms using deep learning

Cha, K. H., Petrick, N., Pezeshk, A. X., Graff, C. G., Sharma, D., Badal, A., & Sahiner, B. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.7.1.012703 · Cited by 46

Are Learned Molecular Representations Ready for Prime Time?Preprint

Yang, K., Swanson, K., Jin, W., Coley, C. W., Eiden, P., Gao, H., Guzmán-Pérez, A., Hopper, T., Kelley, B. P., Mathea, M., et al. (2019) · ChemRxiv · doi:10.26434/chemrxiv.7940594.v1 · Cited by 27

Exploring drug space with ChemMaps.com

Borrel, A., Kleinstreuer, N., & Fourches, D. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty412 · Cited by 16

Enhancing reaction-based de novo design using a multi-label reaction class recommender

Ghiandoni, G. M., Bodkin, M. J., Chen, B., Hristozov, D., Wallace, J. E. A., Webster, J., & Gillet, V. J. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00300-6 · Cited by 16

Diversifying chemical libraries with generative topographic mapping

Lin, A., Beck, B., Horvath, D., Marcou, G., & Varnek, A. A. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00215-x · Cited by 14

Focused Library Generator: case of Mdmx inhibitors

Xia, Z., Karpov, P., Popowicz, G. M., & Tetko, I. V. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00242-8 · Cited by 10

AI developers tout revolution, drugmakers talk evolution

Morrison, C. (2019) · Nature Biotechnology · doi:10.1038/d41587-019-00033-4 · Cited by 8

ChemInform Abstract: Validation of Reaction Vectors for de novo Design

Hristozov, D., Bodkin, M. J., Chen, B., Patel, H., & Gillet, V. J. (2012) · ChemInform · doi:10.1002/chin.201250271 · Cited by 4

Correction to: BRADSHAW: a system for automated molecular design

Green, D. V. S., Pickett, S. D., Luscombe, C. N., Senger, S., Marcus, D., Meslamani, J. E., Brett, D., Powell, A., & Masson, J. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00243-7 · Cited by 2

QSAR: Hansch Analysis and Related Approaches, H. Kubiny, VCH, Weinheim 1993. 232 Seiten, 60 Abb. und 32 Tab. 158,– DM. ISBN 3‐527‐30035‐X

Holzgrabe, U. (1994) · Pharmazie in unserer Zeit · doi:10.1002/pauz.19940230316 · Cited by 1

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473

Critical assessment of methods of protein structure prediction (CASP)—Round XIV

Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26237 · Cited by 503

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet

Bender, A., & Cortés-Ciriano, I. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.12.009 · Cited by 405

Application of Artificial Intelligence in Medicine: An Overview

Liu, P.-R., Lu, L., Zhang, J.-Y., Huo, T.-T., Liu, S.-X., & Ye, Z.-W. (2021) · Current Medical Science · doi:10.1007/s11596-021-2474-3 · Cited by 258

Natural product drug discovery in the artificial intelligence era

Saldívar‐González, F. I., Aldas-Bulos, V. D., Medina‐Franco, J. L., & Plisson, F. (2021) · Chemical Science · doi:10.1039/d1sc04471k · Cited by 235

Artificial intelligence in molecular de novo design: Integration with experiment

Janet, J. P., Mervin, L., & Engkvist, O. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102575 · Cited by 25

Artificial Intelligence Approaches for Rational Drug Design and Discovery

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Artificial intelligence in drug design

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Artificial Intelligence in Drug Discovery: Machine Learning for Molecular Design

Mahmoud, A. R. (2026) · doi:10.13140/rg.2.2.12065.57442

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Joshi, R. P., & Kumar, N. (2021) · Molecules · doi:10.3390/molecules26226761 · Cited by 55