Awesome De novo Drug Design Papers 🔇

Papers about De novo Drug Design 💊 collected by @HUBioDataLab members.

Please feel free to add good, related papers. If there is any error about links, don't hesitate to pull!

2022

• [ACS JCIM 2022] HyFactor: A Novel Open-Source, Graph-Based Architecture for Chemical Structure Generation [Paper] [Code]
• [Journal of Cheminformatics 2022] Designing optimized drug candidates with Generative Adversarial Network [Paper] [Code]
• [arXiv 2022] A Survey on Deep Graph Generation: Methods and Applications [Paper]
• [Nature Machine Intelligence 2022] A deep generative model enables automated structure elucidation of novel psychoactive substances [Paper] [Code]
• [arXiv 2022] Equivariant Diffusion for Molecule Generation in 3D [Paper] [Code]
• [arXiv 2022] Top-N: Equivariant set and graph generation without exchangeability [Paper] [Code]
• [ACS JCIM 2022] RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software [Paper]
• [ChemRxiv 2022] Conditional 𝛽-VAE for De Novo Molecular Generation [Paper]

2021

• [RSC 2021] Attention-based generative models for de novo molecular design [Paper][Code]
• [Nature Communications 2021] Deep generative neural network for accurate drug response imputation [Paper][Code]
• [Journal of Molecular Modeling 2021] Generative chemistry: drug discovery with deep learning generative models [Paper]
• [arXiv 2021] A Graph VAE and Graph Transformer Approach to Generating Molecular Graphs [Paper]
• [Journal of Chemical Information and Modeling 2021] OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design [Paper][Code]
• [arXiv 2021] Transformers for Molecular Graph Generation [Paper][Code]
• [ACS JCIM 2021] Exploring Graph Traversal Algorithms in Graph-Based Molecular Generation [Paper][Code]
• [Nature 2021] Synthon-based ligand discovery in virtual libraries of over 11 billion compounds [Paper]
• [ACS JCIM 2021] Comparative Study of Deep Generative Models on Chemical Space Coverage [Paper]
• [ACS JCIM 2021] Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention [Paper]
• [ACS JCIM 2021] De Novo Structure-Based Drug Design Using Deep Learning [Paper]

2020

• [Royal Society of Chemistry 2020] Artificial Intelligence in Drug Discovery
• [RSC 2020] Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES [Paper][Code]
• [ChemRxiv 2020] Comparative Study of Deep Generative Models on Chemical Space Coverage [Paper][Code]
• [CUoT 2020] Comparison of State-of-the-art Algorithms for de novo Drug Design [Paper][Code]
• [arXiv 2020] Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations [Paper]
• [Nature Machine Intelligence 2020] Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks [Paper][Code]
• [arxiv 2020] Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models [Paper]
• [Drug Discovery Today: Technologies 2020] Graph-based generative models for de Novo drug design [Paper]
• [arXiv 2020] MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning [Paper]
• [ChemRxiv 2020] Practical Notes on Building Molecular Graph Generative Models [Paper][Code]
• [arXiv 2020] RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design [Paper]
• [arXiv 2020] Scalable Deep Generative Modeling for Sparse Graphs [Paper][Code]
• [arXiv 2020] Seq2Mol: Automatic design of de novo molecules conditioned by the target protein sequences through deep neural networks [Paper]
• [arXiv 2020] Target-specific and selective drug design for covid-19 using deep generative models [Paper]
• [ChemRxiv 2020] REINVENT 2.0 – an AI Tool for De Novo Drug Design [Paper][Code]
• [Front. Pharmacol. 2020] Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders [Paper][Code]
• [ACS 2020] The Synthesizability of Molecules Proposed by Generative Models [Paper][Code]

2019

• [Journal of Cheminformatics 2019] A de novo molecular generation method using latent vector based generative adversarial network [Paper][Code]
• [bioRxiv 2019] Accelerating Protein Design Using Autoregressive Generative Models [Paper][Code]
• [Nature Machine Intelligence 2019] Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis [Paper]
• [arXiv 2019] Black Box Recursive Translations for Molecular Optimization [Paper]
• [arXiv 2019] ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations [Paper][PapersWithCode]
• [Bioinformatics 2019] Computational modeling of cellular structures using conditional deep generative networks [Paper][Code]
• [Journal of Chemical Information and Modeling 2019] Conditional Molecular Design with Deep Generative Models [Paper][Code]
• [Journal of Chemical Information and Modeling 2019] De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping [Paper]
• [bioRxiv 2019] Decomposing Retrosynthesis into Reactive Center Prediction and Molecule Generation [Paper]
• [arXiv 2019] Deep learning for molecular design—a review of the state of the art [Paper]
• [Future medicinal chemistry 2019] Deep learning for molecular generation [Paper]
• [Journal of Chemical Information and Modeling 2019] Deep Learning in Chemistry [Paper]
• [Journal of chemical information and modeling 2019] Drug Analogs from Fragment-Based Long Short-Term Memory Generative Neural Networks [Paper]
• [RSC 2019] Efficient Multi-Objective Molecular Optimization in a Continuous Latent Space [Paper][Code]
• [Journal of Cheminformatics 2019] Exploring the GDB-13 chemical space using deep generative models [Paper][Code]
• [Advances in Neural Information Processing Systems 2019] Generative models for graph-based protein design [Paper][Code]
• [bioRxiv 2019] Improving protein function prediction with synthetic feature samples created by generative adversarial networks [Paper][Code]
• [arXiv 2019] Likelihood-Free Inference and Generation of Molecular Graphs [Paper][Code]
• [arXiv 2019] A Model to Search for Synthesizable Molecules [Paper][Code]
• [ACS Central Science 2019] Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction [Paper][Code]
• [arXiv 2019] MolecularRNN: Generating realistic molecular graphs with optimized properties [Paper][PapersWithCode]
• [ChemRxiv 2019] Multi-Resolution Autoregressive Graph-to-Graph Translation for Molecules [Paper]
• [Journal of Computer-Aided Molecular Design 2019] Multi-task generative topographic mapping in virtual screening [Paper]
• [Journal of Cheminformatics 2019] Randomized SMILES Strings Improve the Quality of Molecular Generative Models [Paper][Code]
• [arXiv 2019] Scaffold-based molecular design using graph generative model [Paper]
• [ACS 2019] Shape-Based Generative Modeling for de Novo Drug Design [Paper][Code]
• [arXiv 2019] A Two-Step Graph Convolutional Decoder for Molecule Generation [Paper]

2018

• [ACS Central Science 2018] Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules [Paper][Code]
• [Science Advances 2018] Deep reinforcement learning for de novo drug design [Paper][Code]
• [Journal of Chemical Information and Modeling 2018] Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery [Paper][Code]
• [Proceedings of the AAAI conference on artificial intelligence 2018] Graphgan: Graph representation learning with generative adversarial nets [Paper][Code]
• [arXiv 2018] Learning deep generative models of graphs [Paper][PapersWithCode]
• [arXiv 2018] MolGAN: An implicit generative model for small molecular graphs [Paper][PapersWithCode]
• [Journal of Cheminformatics 2018] Multi-objective de novo drug design with conditional graph generative model [Paper][Code]
• [Nature 2018] Planning chemical syntheses with deep neural networks and symbolic AI [Paper]
• [ACS Medicinal Chemistry Letters 2018] Transforming Computational Drug Discovery with Machine Learning and AI [Paper]
• [Journal of chemical information and modeling 2018] Sparse Generative Topographic Mapping for Both Data Visualization and Clustering [Paper][Code]
• [ACL Anthology 2018] Bidirectional Generative Adversarial Networks for Neural Machine Translation [Paper]
• [KDD 2018] Learning Deep Network Representations with Adversarially Regularized Autoencoders [Paper]
• [arXiv 2018] NetGAN: Generating Graphs via Random Walks [Paper] [Code]
• [Journal of Cheminformatics 2018] Molecular generative model based on conditional variational autoencoder for de novo molecular design [Paper] [Code]

2017

• [Molecular Pharmaceutics 2017] druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico [Paper]
• [ACS Central Science 2017] Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks [Paper]
• [Journal of Cheminformatics 2017] Molecular de-novo design through deep reinforcement learning [Paper][Code]
• [arXiv 2017] GraphGAN: Graph Representation Learning with Generative Adversarial Nets [Paper] [Code]