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Token-Mol 1.0：tokenized drug design with large language model
Codebase and CLI for PLAPT: A state-of-the-art protein-ligand binding affinity model for drug discovery
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
Repository for SMILES-based RNNs for reinforcement learning-based de novo molecule generation
An automated scoring function to facilitate and standardize the evaluation of goal-directed generative models for de novo molecular design
A collection of graph foundation models including papers, codes, and datasets.
Due to the huge vocaburary size (151,936) of Qwen models, the Embedding and LM Head weights are excessively heavy. Therefore, this project provides a Tokenizer vocabulary shearing solution for Qwen…
List of molecules (small molecules, RNA, peptide, protein, enzymes, antibody, and PPIs) conformations and molecular dynamics (force fields) using generative artificial intelligence and deep learning
A collection of AWESOME things about LLM-Centric-Molecular-Discovery.
a Large-Scale Multi-Modal Dataset Containing 20 Million Descriptions
[Arxiv] Discrete Diffusion in Large Language and Multimodal Models: A Survey
A curated list of papers related to molecular diffusion models.
List of Molecular and Material design using Generative AI and Deep Learning
Explore a comprehensive collection of resources, tutorials, papers, tools, and best practices for fine-tuning Large Language Models (LLMs). Perfect for ML practitioners and researchers!
[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support Llama-3/3.1, Llama-2, LLaMA, BLOOM, Vicuna, Baichuan, TinyLlama, etc.
Ying Nian Wu's UCLA Statistical Machine Learning Tutorial on generative modeling.
[NeurIPS 2022] A Fast Post-Training Pruning Framework for Transformers
Structured Neuron Level Pruning to compress Transformer-based models [ECCV'24]
A Survey on Multimodal Retrieval-Augmented Generation
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL.
links to conference publications in graph-based deep learning
Must-read papers on graph neural networks (GNN)