Target Aware De Novo Generation

Active-learning

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

Auto-regressive NN

• 3D-Generative-SBDD: Focuses on structure-based drug design (SBDD) using a 3D generative model to sample molecules for specific protein pockets.
  

Blog Post

• Structure-aware generative molecular design: from 2D or 3D?:
  

Diffusion

• BindDM: BindDM extracts subcomplex from protein-ligand complex, and utilizes it to enhance the binding-adaptive 3D molecule generation in complex
  
• 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
  
• NucleusDiff: Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design
  
• TargetDiff: 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (standalone, 2024).
  

Explicit Hydrogen Bond Interaction Guidance

• DiffInt: Diffusion Model for Structure-Based Drug Design with Explicit Hydrogen Bond Interaction Guidance
  

Docking-based

• ClickGen: deep learning model that utilizes modular reactions like click chemistry to assemble molecules
  
• CReM-dock: de novo design of synthetically feasible compounds guided by molecular docking
  
• Csearch: chemical space search via virtual synthesis and global optimization
  
• 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: modular framework for computer-aided ligand-binding design
  
• 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
  

GAN

• SINGA: SINGA is a Molecular Sampling model with Protein-Ligand Interactions aware Generative Adversarial Network, focusing on generating molecules considering their interactions with proteins.
  

GPT

• 3DSMILES-GPT: 3D molecular pocket-based generation
  
• 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.
  
• PETrans: PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning
  

Graph

• Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
  
• PocketFlow: data-and-knowledge driven structure-based molecular generative model
  
• TargetSA: Adaptive Simulated Annealing for Target-Specific Drug Design
  

Interaction fingerprint constrained

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

Language Model

• DrugGEN: Drug Discovery with Large Language Models and Reinforcement Learning Feedback
  
• Lingo3DMol: A pocket-based 3D molecule generation method that combines language model capabilities with 3D coordinate generation and geometric deep learning.
  

MPNN

• ResGen: pocket-aware 3D molecular generation model based on parallel multiscale modelling
  

Multi-modal

• DRAGONFLY: Prospective de novo drug design with deep interactome 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: structure-based prediction and optimization tool for ligand generation on hard-to-drug targets
  

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

• 3D Structure-based Generative Small Molecule Drug Design: Are We There Yet?:
  
• 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
  
• DiffHopp: Graph Diffusion Model for Novel Drug Design via Scaffold Hopping
  
• TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models
  

Sequence-based

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

Transformer

• AlphaDrug: protein target specific de novo molecular generation (generative chemistry) (standalone).
  
• PCMol: A multi-target model for de novo molecule generation, using AlphaFold protein embeddings for thousands of protein targets.
  

VAE

• 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.
  
• RELATION: A software for DL-based de novo drug design, focusing on generating molecules based on target protein interactions.
  

Zero Shot

• DESERT: Zero-shot 3D drug design by sketching and generating, as presented at NeurIPS 2022.