药物分子设计方法学基本原理

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• 英文论文题名：Progress in HPC-Supported CADD Methodology
• 论文作者：徐峻
• 英文论文作者：Jun Xu ; Research Center for Drug Discovery ; School of Pharmaceutical Sciences ; Sun Yat-Sen University
• 年：2016
• 作者机构：中山大学药学院药物分子设计研究中心;
• 论文关键词：计算机辅助药物设计 ; 超级计算 ; 大数据 ; 化学基元学
• 会议召开时间：2016-07-01
• 会议录名称：中国化学会第30届学术年会摘要集-第二十五分会：化学信息学与化学计量学
• 语种：中文
• 分类号：TQ460.1
• 学会代码：ZGHY
• 会议名称：中国化学会第30届学术年会-第二十五分会 ; 化学信息学与化学计量学
• 会议地点：中国辽宁大连
• 主办单位：中国化学会
• 学会名称：中国化学会
• 页数：2
• 文件大小：206k
• 原文格式：D
• 会议级别：全国

摘要

本文首先介绍药物分子设计新理论——化学基元学~(1,2)。在此基础上,总结我们在超级计算与大数据挖掘技术支持下发展的新CADD理论和技术,包括:超算支持下的基于分子动力学的虚拟筛选方法(MDVS)~3,基于GPU的分子三维叠合并行算法gWEGA~4,面向系统性疾病治疗药物设计的药理网络、分子活性构象预测的各种技术、贝叶斯算法在生物化学大数据挖掘中的应用、三维药效团-受体映射图——3DQSAR模型与解析的新方法~(5-8)。
A new method,Chemomics,is introduced~(1,2).Based upon the methodology,this paper will report new CADD technology supported by HPC(High Performance Computing) including(1) molecular dynamics-based virtual screening(MDVS)~3,(2) GPU-supported algorithm for enhancing molecular shape comparison by Weighted Gaussian Functions(gWEGA)~4,(3) pharmacological network for designing drugs against systemic diseases,(4)methods predicting active molecular conformations in difficult situations,(5) Bayesian Learning techniques for mining biochemical big data,and(6) 3D-pharmacophore-receptor maps for 3DQSAR model validation and elucidation~(5-8).

引文

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