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

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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 · see citing works

MoleculeNet: a benchmark for molecular machine learning

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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 · see citing works

A Deep Learning Approach to Antibiotic Discovery

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ChEMBL: towards direct deposition of bioassay data

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

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Robust physical-world attacks on deep learning visual classification

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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 2,087 · see citing works

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 · see citing works

Planning chemical syntheses with deep neural networks and symbolic AI

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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 · see citing works

Junction Tree Variational Autoencoder for Molecular Graph GenerationPreprint

Jin, W.-G., Barzilay, R., & Jaakkola, T. (2018) · International Conference on Machine Learning · doi:10.48550/arxiv.1802.04364 · Cited by 1,783 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

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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 1,358 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Navigating chemical space for biology and medicine

Lipinski, C. A., & Hopkins, A. L. (2004) · Nature · doi:10.1038/nature03193 · Cited by 1,008 · see citing works

A Mathematical Contribution to Structure-Activity Studies

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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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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

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

The Chemical Space Project

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

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 · see citing works

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 &amp; Engineering · doi:10.1039/c9me00039a · Cited by 683 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Methods and Principles in Medicinal Chemistry

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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 499 · see citing works

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 · see citing works

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 · see citing works

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 422 · see citing works

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 395 · see citing works

Why deep-learning AIs are so easy to fool

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

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 323 · see citing works

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 306 · see citing works

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 298 · see citing works

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 295 · see citing works

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 287 · see citing works

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 · see citing works

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 262 · see citing works

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 · see citing works

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 245 · see citing works

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 245 · see citing works

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 · see citing works

Extraction of chemical structures and reactions from the literature

Lowe, D. M. (2012) · Apollo (University of Cambridge) · doi:10.17863/cam.16293 · Cited by 233 · see citing works

New drugs cost US$2.6 billion to develop

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

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 227 · see citing works

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

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Virtual Chemical Libraries

Walters, W. P. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b01048 · Cited by 202 · see citing works

Data integration: challenges for drug discovery

Searls, D. B. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1608 · Cited by 201 · see citing works

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 185 · see citing works

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 148 · see citing works

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 · see citing works

Cybernetics and forecasting techniques

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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 · see citing works

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 · see citing works

Knowledge-Based Approach to <i>de Novo</i> 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 · see citing works

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 · see citing works

A General Method for Exploiting QSAR Models in Lead Optimization

Lewis, R. A. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049228d · Cited by 78 · see citing works

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 78 · see citing works

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 75 · see citing works

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 · see citing works

Adversarial Controls for Scientific Machine Learning

Chuang, K. V., & Keiser, M. J. (2018) · ACS Chemical Biology · doi:10.1021/acschembio.8b00881 · Cited by 67 · see citing works

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 · see citing works

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 64 · see citing works

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 · see citing works

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 56 · see citing works

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

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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 43 · see citing works

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 · see citing works

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 · see citing works

Exploring drug space with ChemMaps.com

Borrel, A., Kleinstreuer, N., & Fourches, D. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty412 · Cited by 15 · see citing works

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 10 · see citing works

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 · see citing works

AI developers tout revolution, drugmakers talk evolution

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

ChemInform Abstract: Validation of Reaction Vectors for de novo Design

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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 · see citing works

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

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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 · see citing works

Critical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp>

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

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 · see citing works

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 · see citing works

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 227 · see citing works

Generating Multibillion Chemical Space of Readily Accessible Screening Compounds

Grygorenko, O. O., Radchenko, D. S., Dziuba, I., Chuprina, A., Gubina, K. E., & Moroz, Y. S. (2020) · iScience · doi:10.1016/j.isci.2020.101681 · Cited by 223 · see citing works

Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World

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Role of artificial intelligence in revolutionizing drug discovery

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Molecular Screening and Toxicity Estimation of 260,000 Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) through Machine Learning

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Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization

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The Role of Artificial Intelligence in Drug Discovery and Development

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Activity Prediction of Small Molecule Inhibitors for Antirheumatoid Arthritis Targets Based on Artificial Intelligence

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Identification of Bioisosteric Substituents by a Deep Neural Network

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A comprehensive review of artificial intelligence for pharmacology research

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From pilots to decision systems: embedding generative AI into strategic decision-making through a socio-technical and governance lens

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Strategies in Medicinal Chemistry to Discover New Hit Compounds against Ebola Virus: Challenges and Perspectives in Drug Discovery

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Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage

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Contaminants in the Water Environment: Significance from the Perspective of the Global Environment and Health

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Prediction of Chromatography Conditions for Purification in Organic Synthesis Using Deep Learning

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Text‐based representations with interpretable machine learning reveal structure–property relationships of polybenzenoid hydrocarbons

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Crystal structure prediction of materials with high symmetry using differential evolution

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Radical Scavenging Activity and Pharmacokinetic Properties of Coumarin–Hydroxybenzohydrazide Hybrids

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The impact of artificial intelligence on the knowledge, attitude, and practice of pharmacists across diverse settings: A cross-sectional study

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From Pipeline to Plant Protection Products: Using New Approach Methodologies (NAMs) in Agrochemical Safety Assessment

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Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials

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The Brazilian compound library (BraCoLi) database: a repository of chemical and biological information for drug design

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Highly selective and AI-predictable Se–N exchange chemistry between benzoselenazolones and boronic acids for programmable, parallel, and DNA-encoded library synthesis

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Characterizing emerging companies in computational drug development

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HS-DTI: Drug-target Interaction Prediction Based on Hierarchical Networks and Multi-order Sequence Effect

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AI-Driven Drug Discovery and Development

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Aiming to Enhance Higher Education Practice Through Generative AI Integration: A Theoretical Exploration of Critical Success Factors

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Interpretable Molecular Property Predictions Using Marginalized Graph Kernels

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Predicting partition coefficients for the SAMPL7 physical property challenge using the ClassicalGSG method

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Examining the Reliability of ChatGPT: Identifying Retracted Scientific Literature and Ensuring Accurate Citations and References

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Finding the most potent compounds using active learning on molecular pairs

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A comprehensive review on the application of artificial intelligence in drug discovery.

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ARTIFICIAL INTELLIGENCE IN PHARMACY DRUG DESIGN

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Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

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Novel Reagent Space: Identifying Unorderable but Readily Synthesizable Building Blocks

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Molecular Representations in Machine-Learning-Based Prediction of PK Parameters for Insulin Analogs

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Machine Learning-Assisted Iterative Screening for Efficient Detection of Drug Discovery Starting Points.

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The Application of Artificial Intelligence and Drug Repositioning for the Identification of Fibroblast Growth Factor Receptor Inhibitors: A Review

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From Plants to Patients: Advancing Cancer Therapy With Bioengineered Exosomes and AI ‐Driven Innovations

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Harnessing artificial intelligence for the analysis of complex chemical combinations, paving the way for novel flavors in food manufacturing: A comprehensive review

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Recent Development, Applications, and Patents of Artificial Intelligence in Drug Design and Development

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MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery

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History and Present Scenario of Computers in Pharmaceutical Research and Development

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Teaching Computer-Aided Drug Design Using TeachOpenCADD

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A Blockchain-Empowered Federated Learning System and the Promising Use in Drug Discovery

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Pharmacophore Mapping: An Important Tool in Modern Drug Design and Discovery

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Design and discovery of monopolar spindle kinase 1 (MPS1/TTK) inhibitors by computational approaches

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Revolutionizing Drug Discovery: A Systematic Review of AI and Machine Learning Application

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Enabling Synthetically Feasible Molecular Editing in Drug Discovery via Reaction-Regulated Graph-Based Genetic Algorithms

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The Role of Artificial Intelligence in Therapeutic Drug Monitoring and Clinical Toxicity

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AI and experimental convergence: a synergistic pathway to JAK2 inhibitor discovery

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