Papers about De novo Drug Design 💊

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

• [AAAI 2023] MDM: Molecular Diffusion Model for 3D Molecule Generation [Paper] [Code]
• [ChemRxiv 2023] MoFlowGAN: Combining adversarial and likelihood learning for targeted molecular generation [Paper] [Code]
• [arXiv 2023] Graph Generative Model for Benchmarking Graph Neural Networks [Paper] [Code]
• [bioRxiv 2023] Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning [Paper] [Code]
• [arXiv 2023] Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation [Paper] [Code]
• [arXiv 2023] Molecule Design by Latent Prompt Transformer [Paper]
• [Journal of Cheminformatics 2023] ScaffoldGVAE: scaffold generation and hopping of drug molecules via a variational autoencoder based on multi-view graph neural networks [Paper] [Code]
• [Journal of Cheminformatics 2023] MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules [Paper] [Code]
• [arXiv 2023] Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges [Paper]
• [ICML 2023] Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D [Paper] [Code]
• [ACS JCIM 2023] De Novo Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization [Paper] [Code]
• [ECML PKDD 2023] Target-Aware Molecular Graph Generation [Paper]
• [ECML PKDD 2023] SpotGAN: A Reverse-Transformer GAN Generates Scaffold-Constrained Molecules with Property Optimization [Paper] [Code]
• [Journal of Cheminf. 2023] Conditional reduction of the loss value versus reinforcement learning for biassing a de-novo drug design generator [Paper] [Code]
• [ACS JCIM 2023] Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry [Paper] [Code]
• [Research Square 2023] LS-MolGen: Ligand-and-Structure Dual-driven Deep Reinforcement Learning for Target-specific Molecular Generation Improves Binding Affinity and Novelty [Paper] [Code]
• [bioRxiv 2023] An Equivariant Generative Framework for Molecular Graph-Structure Co-Design [Paper] [Code]
• [ChemRxiv 2023] De Novo Design of Molecules Towards Biased Properties via a Deep Generative Framework and Iterative Transfer Learning [Paper] [Code]
• [Bioinformatics 2023] De novo drug design by iterative multiobjective deep reinforcement learning with graph-based molecular quality assessment [Paper] [Code]
• [arXiv 2023] Balancing Exploration and Exploitation: Disentangled β-CVAE in De Novo Drug Design [Paper]
• [ACS JCIM 2023] Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark [Paper] [Code]
• [Molecules 2023] cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation [Paper] [Code]
• [ChemRxiv 2023] A Protein-Ligand Interaction-focused 3D Molecular Generative Framework for Generalizable Structure-based Drug Design [Paper] [Code]
• [arXiv 2023] SILVR: Guided Diffusion for Molecule Generation [Paper] [Code]
• [Journal of Chem. 2023] Deep generative model for drug design from protein target sequence [Paper] [Code]
• [Journal of Mol. Mod. 2023] De novo drug design based on Stack-RNN with multi-objective reward-weighted sum and reinforcement learning [Paper] [Code]
• [ACS 2023] Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity [Paper] [Code]
• [bioRxiv 2023] Variational graph encoders: a surprisingly effective generalist algorithm for holistic computer-aided drug design [Paper] [Code]
• [ChemRxiv 2023] ChemTSv2: Democratizing Functional Molecular Design Using de novo Molecule Generator [Paper] [Code]
• [arXiv 2023] Generative Diffusion Models on Graphs: Methods and Applications [Paper]
• [bioRxiv 2023] A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets [Paper] [Data]
• [arXiv 2023] De Novo Molecular Generation via Connection-aware Motif Mining [Paper] [Code]
• [ICLR 2023] 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction [Paper] [Code]
• [ScienceDirect 2023] Structure-based drug design with geometric deep learning [Paper]
• [ACS 2023] Chemistry42: An AI-Driven Platform for Molecular Design and Optimization [Paper] [Code] [Platform]
• [Journal of Cheminformatics 2023] DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning [Paper] [Code]
• [arXiv 2023] Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks [Paper] [Code]
• [Wiley 2023] De novo design of a molecular catalyst using a generative model [Paper] [Code]
• [ScienceDirect 2023] DNMG: Deep molecular generative model by fusion of 3D information for de novo drug design [Paper] [Code]
• [ACS 2023] Universal Approach to De Novo Drug Design for Target Proteins Using Deep Reinforcement Learning [Paper]
• [ScienceDirect 2023] Chemical language models for de novo drug design: Challenges and opportunities [Paper]
• [arXiv 2023] Drug design on quantum computers [Paper]
• [Nature Communications 2023] Leveraging molecular structure and bioactivity with chemical language models for de novo drug design [Paper]
• [Int. J. Mol. Sci. 2023] PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning [Paper] [Code]
• [Wiley Interdisciplinary Reviews 2023] Graph neural networks for conditional de novo drug design [Paper]
• [Bioinformatics 2023] HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures [Paper] [Code]
• [arXiv 2023] Fragment-based t-SMILES for de novo molecular generation [Paper] [Code]
• [J. Chem. Inf. Model. 2023] De Novo Direct Inverse QSPR/QSAR: Chemical Variational Autoencoder and Gaussian Mixture Regression Models [Paper]
• [bioRxiv 2023] A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets [Paper]
• [Bioinformatics 2023] Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer [Paper] [Code]

• [NeurIPS 2022] Zero-Shot 3D Drug Design by Sketching and Generating [Paper] [Code]
• [arXiv 2022] Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design [Paper] [Code]
• [ICML 2022] Generating 3D Molecules for Target Protein Binding [Paper] [Code]
• [arXiv 2022] Structure-based drug discovery with deep learning | Review [Paper]
• [arXiv 2022] DiffLinker: Equivariant 3D-Conditional Diffusion Model for Molecular Linker Design [Paper] [Code]
• [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]
• [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]

• [scientific report 2021] Transformer neural network for protein-specific de novo drug generation as a machine translation problem [Paper][Code]
• [RSC 2021] Attention-based generative models for de novo molecular design [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]
• [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]

• [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] 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]

• [Journal of Cheminformatics 2019] A de novo molecular generation method using latent vector based generative adversarial network [Paper][Code]
• [Nature Machine Intelligence 2019] Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis [Paper]
• [arXiv 2019] ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations [Paper][PapersWithCode]
• [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]
• [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] 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]
• [arXiv 2019] Likelihood-Free Inference and Generation of Molecular Graphs [Paper][Code]
• [arXiv 2019] A Model to Search for Synthesizable Molecules [Paper][Code]
• [arXiv 2019] MolecularRNN: Generating realistic molecular graphs with optimized properties [Paper][PapersWithCode]
• [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]

• [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]
• [ACS JCIM 2018] Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery [Paper][Code]
• [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]
• [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]
• [Journal of Cheminformatics 2018] Molecular generative model based on conditional variational autoencoder for de novo molecular design [Paper] [Code]

• [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]

• Papers between 2017-2021 about De novo Drug Design 💊 collected by @HUBioDataLab members while I was in research intern program.

• Template by @mengliu1998!