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A powerful tool for creating fine-tuning datasets for LLM
An iterative multi-LLM consensus framework for accurate cell type annotation in single-cell RNA-seq data
LLMs-from-scratch项目中文翻译
仅需Python基础,从0构建大语言模型;从0逐步构建GLM4\Llama3\RWKV6, 深入理解大模型原理
RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.
HoTS: Sequence-based prediction of binding regions and drug-target interactions.
🔥 1Panel provides an intuitive web interface and MCP Server to manage websites, files, containers, databases, and LLMs on a Linux server.
Chemical representation learning paper in Digital Discovery
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
ChEMBL Database used to create Lipinski Descriptors (ADME Pharmokinetic Profile) to use in a Random Forest Regression Model
Using RDKIT and SciKit-Learn to analyse Herg data from Chembl
Deep Reinforcement Learning for de-novo Drug Design
Prediction of LogP from SMILES datasets of chemical molecules
RDKit related blog posts, notebooks, and data.
在学习RDKit,SMLIES,SMARTS时整理的资料,RDKit-tutorials including RDKit Cookbook,The RDKit Book,and SMARTS examples.
rdKit basics (provided jupyter notebooks are custom curated and will help the users to start working on rdKit)
Tutorials to learn how to work with the RDKit
Quantitative Structure- Activity Relationship (QSAR) Prediction - Machine Learning Model
😎 Awesome lists about all kinds of interesting topics
《李宏毅深度学习教程》(李宏毅老师推荐👍,苹果书🍎),PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Implementation of Alphafold 3 from Google Deepmind in Pytorch
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
Generative Adversarial Network: Optimization in Targeted Design
Optimized Generative Adversarial Network with Graph Convolutional Networks for Novel Molecule Design
A curated list of resources for machine learning for small-molecule drug discovery
A tutorial on how to set up a Linux environment on a computer running Windows 10. Followed by how to install and use (bio)conda.