gRNAde: Geometric Deep Learning for 3D RNA inverse design

Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval, Nat Mach Intell 2023 (https://www.nature.com/articles/s42256-023-00759-6)

ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT methods to cover single/multi-node GPUs. Supports default & custom datasets for applications such as summarization and Q&A. Supportin…

open data sets for machine learning pertaining to porous materials

Various Ideas for Protein Masked LM with ESMFold/ESM-2

DiffSeqMol: Diffusion model for molecular sequence generation

Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch

A curated list of awesome self-learning materials in Computational Structural Biology, such as sources, tutorials, etc.

What would you do with 1000 H100s...

Listing of papers about machine learning for proteins.

Simple implementation of OpenAI CLIP model in PyTorch.

Official implementation for HyenaDNA, a long-range genomic foundation model built with Hyena

Must-read papers on graph neural networks (GNN)

A Euclidean diffusion model for structure-based drug design.

Transformer model for structure-agnostic metal-organic frameworks (MOF) property prediction

A minitutorial at the SIAM Conference on Computational Science and Engineering, February 26 - March 3, 2023

Official code for Periodic Graph Transformers for Crystal Material Property Prediction (NeurIPS 2022)

LLM Chain for answering questions from documents with citations

Lightweight wrapper of the official ChatGPT API in your terminal

Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.

A modular framework for neural networks with Euclidean symmetry

NequIP is a code for building E(3)-equivariant interatomic potentials

Official implementation of pre-training via denoising for TorchMD-NET