This is a curated list of "Embodied AI or robot with Large Language Models" research. Watch this repository for the latest updates! 🔥

Creative interactive views of any dataset.

This repository contains setup instructions and the notebooks for the AI in drug discovery workshops

Awesome AI-aided Drug Discovery

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

Scalable Deep Learning for RNA Secondary Structure Prediction

A high-quality tool for convert PDF to Markdown and JSON.一站式开源高质量数据提取工具，将PDF转换成Markdown和JSON格式。

RoseTTAFold2 protein/nucleic acid complex prediction

Chai-1, SOTA model for biomolecular structure prediction

A dataset for training and benchmarking deep learning models for RNA structure prediction

The simplest, fastest repository for training/finetuning medium-sized GPTs.

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…

[ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome

A beautiful, simple, clean, and responsive Jekyll theme for academics

Multi-Task Deep Neural Networks for Natural Language Understanding

S-PLM: Structure-aware Protein Language Model via Contrastive Learning between Sequence and Structure

RiboNucleic Acid (RNA) Language Model

The official Meta Llama 3 GitHub site

RNA Multiple Sequence Alignment

Rotary Transformer

[NeurIPS 2024] BEACON: Benchmark for Comprehensive RNA Tasks and Language Models

🇫🇷 Oh My Tmux! Pretty & versatile tmux configuration / customization made with ❤️

Official implement of paper "Multi-purpose RNA Language Modeling with Motif-aware Pre-training and Type-guided Fine-tuning"

Accelerate Molecular Biology Research with Machine Learning

Nucleic Acids Research 2024：RNA-MSM model is an unsupervised RNA language model based on multiple sequences that outputs both embedding and attention map to match different types of downstream tasks.

RoFormer升级版

Efficiently learn graphs with a Differentiable Adjacency Test (DAT).