drug专题

AI Drug Discovery Design(学习路线)

AIDD,即AI Drug Discovery & Design,是近年来非常火热的技术应用,已经介入到新药设计到研发的大部分环节当中,为新药发现与开发带来了极大的助力。其学习路线涉及多个学科和领域的知识。以下是一个可能的AIDD学习路线,以供参考: 基础数学知识:学习数理统计、概率论和微积分等基础知识。这些数学工具在AIDD中起到至关重要的作用,用于处理和分析大量数据,以及构建和优化算法模型。

【文献阅读】AlphaFold touted as next big thing for drug discovery — but is it?

今天来精读2023年10月发在《Nature》上的一篇新闻:AlphaFold touted as next big thing for drug discovery — but is it? (nature.com)https://www.nature.com/articles/d41586-023-02984-w Questions remain about whether the AI

“bound drug/molecule”or “unbound drug/molecule”、molecule shape、sketching是什么?

“bound drug/molecule”or “unbound drug/molecule” For clarity, the following terms will be used throughout this study: “bound drug/molecule” (or “unbound drug/molecule”) refers to the drug/molecule th

Paper reading (三十七):Generating focused molecule libraries for drug discovery with RNN

论文题目:Generating focused molecule libraries for drug discovery with recurrent neural networks scholar 引用:203 页数:12 发表时间:2017.12 发表刊物:ASC(American Chemical Society) Central Science 作者:Marwin H. S.

药物设计中的人工智能(Artificial Intelligence in Drug Design,2022)

药物设计中的人工智能 Artificial Intelligence in Drug Design 编者:亚历山大·海菲兹 出版商:美国胡马纳出版社 2022年 前言 创新疗法的设计是一个创造性的过程,涉及结合传统和新的分子建模技术同化和分析可用的实验数据。这可能是一个极其复杂、漫长和昂贵的过程。人工智能(AI)和机器学习(ML)方法的应用有望彻底改变设计-制造-测试-分析

Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction

Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction 基本信息 博客创建者 鲁智深 博客贡献人 鲁智深:主要内容介绍 作者 Yingheng Wang, Yaosen Min, Xin Chen, and Ji Wu 标签 multi-view graph

【文章阅读 TODO】Transfer learning for drug–target interaction prediction

Bioinformatics , 2023 Transfer learning for drug–target interaction prediction 本文主要是对迁移学习所使用的三种模式进行学习  Deep transfer learning is applying transfer learning on deep neural networks. The

Pushing the boundaries of molecular representation for drug discovery with graph attention mechanism

AttentiveFP 2020 Motivations 1、The gap between what these neural networks learn and what human beings can comprehend is growing 2、Graph-based representations take only the information concerning th

Chapter4 : Application of Artificial Intelligence and Machine Learning in Drug Discovery

reading notes of《Artificial Intelligence in Drug Design》 文章目录 1.Introduction2.Generative Chemistry3.Target Profiling4.ADMET Prediction and Scoring5.Synthesis Planning6.Conclusion 1.Introduc

2022-Deep generative molecular design reshapes drug discovery-分子生成设计重塑药物发现

文章目录 药物发现中的深度生成模型化合物/分子的表示 Deep Generative Models递归神经网 RNN变分自动编码器 VAE生成性对抗网络 (Generative Adversarial Networks, GANs)Flow-based models强化学习(Reinforcement Learning, RL) 在小分子药物设计中的应用生成有效的小分子生成具有类药物特性的分