PROPMAP: A Novel GPU-accelerated Structure-Property Relationship Mapping Algorithm Utilizing Volunteer Computing

化学文摘服务（CAS）最近在CAS注册表中记录了第5300万种独特的化学物质，其中第4000万种仅在9个月前被编目。随着化学物质的爆炸性增长，出现了这些物质具有哪些物理，化学和生物学特性的问题。现有的基本物理/化学性质的数据，如离解能，logP，形成的时间，折射率，沸点和熔点定义的结构性质关系（SPR），并可通过Scientology和贝尔斯坦。同样，基本的生物学特性，如酶的结合常数可通过ChEMBL和PubChem数据库获得。这项研究创造了一个创新的工具，依靠志愿者计算预测和映射SPRs三维使用一种新的计算算法？PROPMAP？用于教育和研究。PROPMAP的新架构利用了Monte Carlo随机游走结构生成器和基于图形处理单元（GPU）加速的支持向量回归算法的定量结构属性关系（QSPR）模型。PROPMAP交互式地将用户选择的物理、化学和生物特性映射到任何感兴趣的输入化学结构上。图形表示促进了对基础化学教育中SPR的理解，并使研究中有针对性的性质修改成为可能。这种新方法通过伯克利开放式网络计算基础设施（BOINC）利用志愿者GPU计算，以克服训练和交叉验证超过一百万种物质的数据集模型的计算费用。通过万维网接口免费提供该工具，作为教育和研究工具使用，同时还通过讲习班和研讨会直接培训机构使用该工具，从而扩大了PROPMAP对科学界的影响。

The Chemical Abstract Services (CAS) recently recorded the 53 millionth unique chemical substance in the CAS registry with the 40 millionth being cataloged only 9 months prior. With this explosive growth in chemical substances, the question of what physical, chemical, and biological properties are possessed by these substances arises. The existing data for fundamental physical/chemical properties, such as dissociation energies, logP, enthalpies of formation, refractive indexes, boiling points, and melting points define Structure Property Relationships (SPR) and is accessible through SciFinder and Beilstein. Similarly, fundamental biological properties such as binding constants to enzymes are available through the ChEMBL and PubChem databases. This research creates an innovative tool relying on volunteer computing for predicting and mapping SPRs three-dimensionally using a novel computational algorithm ?PROPMAP? for education and research. The novel architecture of PROPMAP utilizes a Monte Carlo random walk structure generator and Quantitative Structure Property Relationship (QSPR) models based on a graphics processing unit (GPU) accelerated Support Vector Regression algorithm. PROPMAP interactively maps user-selected physical, chemical, and biological properties onto any input chemical structure of interest. Graphical representation fosters understanding of SPRs in basic chemistry education and enables targeted property modifications in research. This novel approach utilizes volunteer GPU computing through the Berkeley Open Infrastructure for Network Computing (BOINC) to overcome the otherwise prohibitive computational expense of training and cross validating models for data sets in excess of one million substances. The impact of PROPMAP on the scientific community is broadened by making the tool freely available through a WWW interface for use as an educational and research tool while also directly training institutions on its utility through workshops and seminars.