fourmodern

fourmodern's Stars

• mrdbourke/simple-local-rag

  Build a RAG (Retrieval Augmented Generation) pipeline from scratch and have it all run locally.

  Language:Jupyter Notebook463147

• OmicsML/awesome-molecule-protein-pretrain-papers

  293

• sokrypton/ColabDesign

  Making Protein Design accessible to all via Google Colab!

  Language:Python562131

• tencent-ailab/grover

  This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data

  Language:Python32968

• shuyana/DiffusionProteinLigand

  Language:Python7611

• octavian-ganea/equidock_public

  EquiDock: geometric deep learning for fast rigid 3D protein-protein docking

  Language:Roff23158

• zchwang/IGModel

  This is the first model that can simultaneously predict the RMSD of the ligand docking pose and the binding strength against the target.

  Language:Python141

• milmor/diffusion-transformer-keras

  Implementation of Latent Diffusion Transformer Model in Tensorflow / Keras

  Language:Python14

• jaakkopasanen/AutoEq

  Automatic headphone equalization from frequency responses

  Language:Python13.2k2.5k

• Beomi/Gemma-EasyLM

  Train GEMMA on TPU/GPU! (Codebase for training Gemma-Ko Series)

  Language:Python4511

• boun-tabi/biochemical-lms-for-drug-design

  Code for the paper "Exploiting Pretrained Biochemical Language Models for Targeted Drug Design", to appear in Bioinformatics, Proceedings of ECCB2022.

  Language:Jupyter Notebook172

• sanjaradylov/smiles-gpt

  Generative Pre-Training from Molecules

  Language:Jupyter Notebook155

• teddylee777/langchain-kr

  LangChain 공식 Document, Cookbook, 그 밖의 실용 예제를 바탕으로 작성한 한국어 튜토리얼입니다. 본 튜토리얼을 통해 LangChain을 더 쉽고 효과적으로 사용하는 방법을 배울 수 있습니다.

  Language:Jupyter Notebook1k250

• amorehead/awesome-molecular-generation

  Awesome papers related to generative molecular modeling and design.

  29239

• yuyangw/MolCLR

  Implementation of MolCLR: "Molecular Contrastive Learning of Representations via Graph Neural Networks" in PyG.

  Language:Python24360

• snap-stanford/pretrain-gnns

  Strategies for Pre-training Graph Neural Networks

  Language:Python957161

• bowang-lab/scGPT

  Language:Jupyter Notebook999192

• junxia97/awesome-pretrain-on-molecules

  [IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models

  48451

• jasonyim2/book3

  Language:Jupyter Notebook511

• dimz119/learn-langchain

  Language:Python108

• dengjianyuan/Survey_AI_Drug_Discovery

  30664

• HarryKane11/langchain

  Language:Python1980

• alxfgh/Large-Language-Models-in-Chemistry

  Working collection of papers, repos and models of transformer based language models trained or tuned for the Chemical domain, from natural language to chemical modeling and property prediction

  414

• aws-samples/aws-healthcare-lifescience-ai-ml-sample-notebooks

  Language:Jupyter Notebook9042

• AspirinCode/papers-for-molecular-design-using-DL

  List of Molecular and Material design using Generative AI and Deep Learning

  65498

• rickiepark/Generative_Deep_Learning_2nd_Edition

  <만들면서 배우는 생성 AI 2판>의 코드 저장소

  Language:Jupyter Notebook4628

• eugeneyan/open-llms

  📋 A list of open LLMs available for commercial use.

  11k702

• myeonghak/DGL-tutorial

  그래프 딥러닝 라이브러리 DGL 쉽게 배우기

  Language:Jupyter Notebook669

• luyang93/Gromacs

  gromacs(Protein-Ligand Complex)

  Language:Python114

• hyeshik/colab-biolab

  Language:Jupyter Notebook155