DrugAI资料汇总

DrugAI资料汇总

CADD学习汇总

  • CADD课程学习(1)-- 药物设计基础知识

分子式基础

  • SMILIES基础
  • CADD课程学习(2)-- 靶点晶体结构信息

数据集

  • 分子数据集
  • CADD课程学习(6)-- 获得已有的虚拟化合物库(Drugbank、ZINC)

ChemDraw

  • CADD课程学习(5)-- 构建靶点已知的化合结构(ChemDraw)

蛋白建模

  • CADD课程学习(4)-- 获取没有晶体结构的蛋白(SWISS-Model)

PyMol教程

  • 安装
  • 基础
  • 动画制作
  • surface/cartoon透明度的调节(静电势能)
  • 构建肽链+优化构型
  • 蛋白结合口袋结构比较(pocket alignment)
  • CADD课程学习(3)-- 靶点药物相互作用(PyMol)

AutoDock教程

  • CADD课程学习(7)-- 模拟靶点和小分子相互作用 (半柔性对接 AutoDock)
  • CADD课程学习(7)-- 模拟靶点和小分子相互作用 (柔性对接 AutoDock)

MOE教程

  • CADD课程学习(10)-- 模拟不同体系与小分子相互作用(MOE)
  • CADD课程学习(12)-- 基于碎片的药物设计(MOE)

ZDOCK教程

  • CADD课程学习(10)-- 模拟不同体系与蛋白-蛋白相互作用(ZDOCK)

Open Babel教程

  • CADD课程学习(9)-- 不同类型分子结构转换(Open Babel)

SYBYL教程

  • CADD课程学习(11)-- 构建已有小分子的构效关系模型(SYBYL)

虚拟筛选

  • CADD课程学习(8)-- 化合物库虚拟筛选(Virtual Screening)

Schrodinger教程

Rosetta教程

Rosetta: 了解Rosetta软件包构架(1)
Rosetta的程序的基本步骤(2)
Rosetta基础(3)–Rosetta能量函数简介

GROMACS分子动力学模拟

  • 安装Gromacs-2022 GPU-CUDA加速版 unbantu
  • CADD课程学习(13)-- 研究蛋白小分子动态相互作用(GROMACS)

RDKit教程

入门

  • 安装
  • 分子所具有的自由基电子数、价电子数

评价指标

分子

  • 定量评估类药性(QED)
  • 分子指纹的分子相似性计算
  • 药物分子进行片段分解
  • Murcko骨架聚类化合物库
  • 拓扑极性表面积(TPSA)
  • 合成可行性分数–SA SCORE

蛋白

  • LDDT

算法建模

分子生成(MG)

  • 2016 NIPS | Variational Graph Auto-Encoders
  • 2017 Oncotarget | The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology
  • 2017 Molecular Informatics | Application of generative autoencoder in de novo molecular design
  • 2018 arXiv | Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Mo
  • 2018 Journal of cheminformatics | Molecular generative model based on conditional variational autoencoder for de novo molecular design
  • 2018 arXiv preprint | MolGAN: An implicit generative model for small molecular graphs
  • 2018 ACS | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
  • 2018 ACS | Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
  • 2019 NeurIPS | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
  • 2019 ICLR | Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
  • 2020 Front. Pharmacol | Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
  • 2020 ACM | MoFlow: An Invertible Flow Model for Generating Molecular Graphs
  • CLR 2020 | GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
    • GraphAF源码解读
  • 2021 CIKM |GF-VAE: A Flow-based Variational Autoencoder for Molecule Generation
  • 2022 | Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization
  • 2022 ICML | LIMO: Latent Inceptionism for Targeted Molecule Generation
  • 2022 ICLR | CONTRASTIVE LEARNING OF IMAGE- AND STRUCTURE BASED REPRESENTATIONS IN DRUG DISCOVERY
  • 2022 ICML | Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

3D

  • Equivariant Diffusion for Molecule Generation in 3D
  • GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation
  • GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles

分子优化(MO)

分子属性预测(MPP)

分子毒性预测(MTP)

配体受体亲和力预测(CPI)

  • 2020 Bioinformatics | GraphDTA: predicting drug target binding affinity with graph neural networks
  • 2020 Bioinformatics | TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
  • 2021 RSC | DGraphDTA:Drug–target affinity prediction using graph neural network and contact maps
  • 2022 nature machine intelligence | GLAM: An adaptive graph learning method for automated molecular interactions and properties predictions

蛋白质结构预测

蛋白预测

  • 2022 Science | Scaffolding protein functional sites using deep learning

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