Some solved and unsolved problems of chemoinformatics

The field of chemoinformatics has developed from different roots, starting in the 1960s. These branches have now merged into a scientific discipline of its own, exchanging ideas and methods across different areas of chemistry. In the last 40 years chemoinformatics has achieved a lot. Without access to the databases in chemistry developed with chemoinformatics methods, modern chemical research would not be able to work at its present high level of competence. However, there are quite a few challenges, such as drug design and understanding the effect of chemicals on human health and on the environment, as well as furthering our knowledge of chemistry and of biological systems, that can benefit from a more intensive use of chemoinformatics methods. Approaches to meet these challenges will be briefly outlined. All this emphasizes that chemoinformatics has matured into a scientific discipline of its own that reaches out to many other chemical fields and will increase in attractiveness to students and researchers.     

Chemistry in Times of Artificial Intelligence

Chemists have to a large extent gained their knowledge by doing experiments and thus gather data. By putting various data together and then analyzing them, chemists have fostered their understanding of chemistry. Since the 1960s, computer methods have been developed to perform this process from data to information to knowledge. Simultaneously, methods were developed for assisting chemists in solving their fundamental questions such as the prediction of chemical, physical, or biological properties, the design of organic syntheses, and the elucidation of the structure of molecules. This eventually led to a discipline of its own: chemoinformatics. Chemoinformatics has found important applications in the fields of drug discovery, analytical chemistry, organic chemistry, agrichemical research, food science, regulatory science, material science, and process control. From its inception, chemoinformatics has utilized methods from artificial intelligence, an approach that has recently gained more momentum.     

Basic overview of chemoinformatics

There is no particular point in time that determines when chemoinformatics was founded or established. It slowly evolved from several, often quite humble beginnings. Scientists in various fields of chemistry struggled with the development of computer methods which allowed them to manage the enormous amount of chemical information and to find relationships between the structure and properties of a compound. During the 1960s some early developments appeared that led to a flurry of activities in the 1970s. This review provides a general overview of basic methods in the specific fields of chemoinformatics, from encoding chemical compounds, storing and searching data in databases, to generating and analyzing these data. In addition, the chief interconnecting points of chemoinformatics applications are highlighted including the contributions of Johann Gasteiger to this field.     

Internet推动的化学信息学重要进展*

Internet的普及为专业人员获取数据信息、利用计算工具提供了统一的平台,由此为化学信息学的发展带来了新的空间,推动了化学信息学以网络为基础,以化学相关的数据、信息及计算资源共享为目标的快速发展。本文将从不同侧面回顾近10年来化学信息学的重要进展, 包括：(1) 网络化学信息检索：索引对象从化学浅层网向化学深层网发展;检索工具从Web化学信息资源导航向化学专业搜索引擎(包括文本信息和化合物标识信息)、及化学深层网检索引擎 (化合物物性数据提取)发展;索引粒度从Web站点向页面、乃至页面中的特定内容发展,一般页面特定内容的数据提取(即非结构化数据提取)是未来发展的方向。(2)可共享的化学数据库：从可免费访问和使用的化学数据库向数据库内容通过集成多来源数据(包括数据库拥有者主动收集、多来源数据主动提交达到共享的方式,repository)实现数据库内容免费下载和共享,以及不同数据库之间的相关内容实现无缝连接的方向发展(如NIH建成的药物小分子共享数据库PubChem)。(3) 开源(open source)化学软件工具包：从化学结构基本处理模块如CDK、JOELib向集成开发环境如化学信息学与生物信息学集成环境Bioclipse发展。(4) 与化合物及其数据共享相关的推荐标准：包括用于共享数据交换的化学标记语言CML、IUPAC推荐的学术论文相关热力学实验数据提交标准ThermoML及化合物结构唯一描述码InChI。(5) 计算化学资源共享及基于网格的应用：从可执行程序的下载向在线计算、基于网格的应用发展。(6) eChemistry和虚拟研究环境：网络也成为化学相关日常的科学活动中不可缺少的平台。构建以网络为平台、支持开展科研活动的数字化基础设施和服务的eChemstry探索开始出现,根据需要自主集成多来源数据和计算资源,形成不同层次的支持协同工作的虚拟研究环境是未来数据和计算资源共享方式的发展方向。     

Computer-Assisted Solution of Chemical Problems—The Historical Development and the Present State of the Art of a New Discipline of Chemistry

The topic of this article is the development and the present state of the art of computer chemistry, the computer-assisted solution of chemical problems. Initially the problems in computer chemistry were confined to structure elucidation on the basis of spectroscopic data, then programs for synthesis design based on libraries of reaction data for relatively narrow classes of target compounds were developed, and now computer programs for the solution of a great variety of chemical problems are available or are under development. Previously it was an achievement when any solution of a chemical problem could be generated by computer assistance. Today, the main task is the efficient, transparent, and non-arbitrary selection of meaningful results from the immense set of potential solutions—that also may contain innovative proposals. Chemistry has two aspects, constitutional chemistry and stereochemistry, which are interrelated, but still require different approaches. As a result, about twenty years ago, an algebraic model of the logical structure of chemistry was presented that consisted of two parts: the constitution-oriented algebra of be- and r-matrices, and the theory of the stereochemistry of the chemical identity group. New chemical definitions, concepts, and perspectives are characteristic of this logic-oriented model, as well as the direct mathematical representation of chemical processes. This model enables the implementation of formal reaction generators that can produce conceivable solutions to chemical problems—including unprecedented solutions—without detailed empirical chemical information. New formal selection procedures for computer-generated chemical information are also possible through the above model. It is expedient to combine these with interactive methods of selection. In this review, the Munich project is presented and discussed in detail. It encompasses the further development and implementation of the mathematical model of the logical structure of chemistry as well as the experimental verification of the computer-generated results. The article concludes with a review of new reactions, reagents, and reaction mechanisms that have been found with the PC-programs IGOR and RAIN.     

An evaluation of experimental design in QSAR modelling utilizing the k‐medoid clustering

A reliable selection of a representative subset of chemical compounds has been reported to be crucial for numerous tasks in computational chemistry and chemoinformatics. We investigated the usability of an approach on the basis of the k‐medoid algorithm for this task and in particular for experimental design and the split between training and validation set. We therefore compared the performance of models derived from such a selection to that of models derived using several other approaches, such as space‐filling design and D‐optimal design. We validated the performance on four datasets with different endpoints, representing toxicity, physicochemical properties and others. Compared with the models derived from the compounds selected by the other examined approaches, those derived with the k‐medoid selection show a high reliability for experimental design, as their performance was constantly among the best for all examined datasets. Of all the models derived with all examined approaches, those derived with the k‐medoid approach were the only ones that showed a significantly improved performance compared with a random selection, for all datasets, the whole examined range of selected compounds and for each dimensionality of the search space. Copyright © 2012 John Wiley & Sons, Ltd.     

化学结构正规编码及其信息处理功能

自从计算机在化学领域中应用以来,化学结构的正规编码已广泛用来作为一种化学文献的信息索引,它还在开发化学智能系统中发挥着重要作用。正规编码生成是以化学结构图的拓扑性质为基础的。本文首先概述编码的基本原理及其生成的方法,随后分析这种编码在当今文献数据库、化学反应数据库、分子设计以及合成反应路线规划等化学信息系统中的功能。