Assessing the impact of generative AI on medicinal chemistry

Mastering the game of Go with deep neural networks and tree search

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Applications of machine learning in drug discovery and development

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

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

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Deep learning enables rapid identification of potent DDR1 kinase inhibitors

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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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Computer-based de novo design of drug-like molecules

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GuacaMol: Benchmarking Models for de Novo Molecular Design

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druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

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De Novo Design of Bioactive Small Molecules by Artificial Intelligence

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Adversarial Threshold Neural Computer for Molecular de Novo Design

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Discovery and Optimization of 3-(2-(Pyrazolo[1,5-a]pyrimidin-6-yl)ethynyl)benzamides as Novel Selective and Orally Bioavailable Discoidin Domain Receptor 1 (DDR1) Inhibitors

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Structural Mechanisms Determining Inhibition of the Collagen Receptor DDR1 by Selective and Multi-Targeted Type II Kinase Inhibitors

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Drug Guru: A computer software program for drug design using medicinal chemistry rules

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Autonomous Molecular Design: Then and Now

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Deep Generative Models for Molecular Science

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The scaffold hopping potential of pharmacophores

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Generative Models for Artificially‐intelligent Molecular Design

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A ring‐bracing approach to computer‐assisted ligand design

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Computational Medicinal Chemistry

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Generative artificial intelligence

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Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

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Applications of Deep Learning in Molecule Generation and Molecular Property Prediction

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Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers

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Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

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Reply to ‘Assessing the impact of generative AI on medicinal chemistry’

Zhavoronkov, A. A., & Aspuru‐Guzik, A. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0417-3 · Cited by 24

Generative AI Models for Drug Discovery

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Modern hit-finding with structure-guided de novo design: identification of novel nanomolar adenosine A2A receptor ligands using reinforcement learningPreprint

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Generative Artificial Intelligence in Pharmaceutical Research and Drug Discovery

Sayed, M. A. (2024) · doi:10.13140/rg.2.2.28297.66402

Identification of nanomolar adenosine A2A receptor ligands using reinforcement learning and structure-based drug design

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