Target Aware De Novo Generation

• 3D-Generative-SBDD: Focuses on structure-based drug design (SBDD) using a 3D generative model to sample molecules for specific protein pockets.
  
• AlphaDrug: protein target specific de novo molecular generation (generative chemistry) (standalone).
  
• BindDM: BindDM extracts subcomplex from protein-ligand complex, and utilizes it to enhance the binding-adaptive 3D molecule generation in complex
  
• cMolGPT: cMolGPT, a Conditional Generative Pre-trained Transformer, aims for de novo molecular design by enforcing target specificity during the generation process, emphasizing the incorporation of target embeddings.
  
• DESERT: Zero-shot 3D drug design by sketching and generating, as presented at NeurIPS 2022.
  
• DRAGONFLY:
  
• DrugGEN: Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks
  
• DrugHIVE: Structure-Based Drug Design with a Deep Hierarchical Generative Model
  
• HySonLab: Multimodal protein representation learning and target-aware variational auto-encoders for protein-binding ligand generation
  
• Ligand_Generation: This project focuses on target-aware variational autoencoders for ligand generation, employing multimodal protein representation learning for structure-based drug design.
  
• PCMol: A multi-target model for de novo molecule generation, using AlphaFold protein embeddings for thousands of protein targets.
  
• PETrans: PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning
  
• Pocket2Mol: Uses equivariant graph neural networks for efficient molecular sampling based on 3D protein pockets.
  
• PocketFlow: data-and-knowledge driven structure-based molecular generative model
  
• RELATION: A software for DL-based de novo drug design, focusing on generating molecules based on target protein interactions.
  
• ResGen: pocket-aware 3D molecular generation model based on parallel multiscale modelling (standalone).
  
• SCRdkit: assesses the 3D similarity between generated molecules and a reference molecule
  
• SINGA: SINGA is a Molecular Sampling model with Protein-Ligand Interactions aware Generative Adversarial Network, focusing on generating molecules considering their interactions with proteins.
  

Active-learning

• ChemSpaceAL: an Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation (generative chemistry) (standalone, 2024).
  

Benchmarks

• CBGBench:
  
• From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics: From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics
  

Diffusion

• DiffDec: A model that uses an end-to-end equivariant diffusion process for optimizing molecular structures through scaffold decoration conditioned on 3D protein pockets.
  
• DiffSBDD: Structure-based Drug Design with Equivariant Diffusion Models (standalone, 2024).
  
• MolSnapper: Conditioning Diffusion for Structure Based Drug Design
  
• TargetDiff: 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (standalone, 2024).
  

Docking-based

• DockStream: DockStream: a docking wrapper to enhance de novo molecular design
  
• FREED: Utilizes explorative experience replay in a generative reinforcement learning setup for drug design.
  
• KGDiff: A model for explainable target-aware molecule generation with knowledge guidance.
  
• Lib-INVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design
  
• liGAN: A project for structure-based drug discovery using deep generative models of atomic density grids.
  
• LS-MolGen: A ligand-and-structure dual-driven deep reinforcement learning method for target-specific molecular generation, integrating docking scores and bioactivity data for molecule optimization.
  
• MORLD: Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors
  
• OptiMol: OptiMol : Optimization of binding affinities in chemical space for drug discovery
  
• PMDM: dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets
  
• PocketOptimizer 2.0: Design of small molecule‐binding pockets in proteins (standalone)
  
• RGA: A reinforcement learning-based genetic algorithm for structure-based drug design, introduced at NeurIPS 2022.
  
• SampleDock: Navigating Chemical Space By Interfacing Generative Artificial Intelligence and Molecular Docking
  
• SBMolGen: integrates a recurrent neural network, a Monte Carlo tree search, and docking simulations
  
• SECSE: Systemic evolutionary chemical space exploration for drug discovery
  

Dual-target

• Alx-Fuse: Structure-Aware Dual-Target Drug Design through Collaborative Learning of Pharmacophore Combination and Molecular Simulation
  

Fragment-based

• FragGen: FragGen: towards 3D geometry reliable fragment-based molecular generation
  

Interaction fingerprint constrained

• IFP-RNN: A molecule generative model used interaction fingerprint (docking pose) as constraints.
  

Language Model

• Lingo3DMol: A pocket-based 3D molecule generation method that combines language model capabilities with 3D coordinate generation and geometric deep learning.
  

PROTACs

• PROTACable: Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs (standalone, published 2024).
  

Pharmacophore

• DEVELOP: Deep Generative Design with 3D Pharmacophoric Constraints
  

Physics-based generation

• SPOTLIGHT:
  

Protein-ligand interactions

• DeepICL: 3D molecular generative framework for interaction-guided drug design
  
• DrugGPS: Focuses on learning subpocket prototypes for generalizable structure-based drug design, introduced at ICML 2023.
  
• FLAG: A Fragment-based Ligand Generation framework to generate 3D molecules with valid and realistic substructures fragment-by-fragment.
  
• GraphBP: Implements a method for generating 3D molecules targeting protein binding, presented at ICML 2022.
  

RAG

• Rag2Mol: Rag2Mol: Structure-based drug design based on Retrieval Augmented Generation
  

Reviews

• Docking-based generative approaches in the search for new drug candidates [2022]: Docking-based generative approaches in the search for new drug candidates
  
• Generative Deep Learning for Targeted Compound Design [2021]: Generative Deep Learning for Targeted Compound Design
  
• Integrating structure-based approaches in generative molecular design [2023]: Integrating structure-based approaches in generative molecular design
  
• Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?: benchmark to evaluate the performance of sixteen models across these different algorithmic foundations by assessing the pharmaceutical properties of the generated molecules and their docking affinities with specified target proteins
  

Scaffold Hopping

• DeepHop: Deep scaffold hopping with multimodal transformer neural networks
  

Sequence-based

• Bajorath_Gen: Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model