2013 Computer-Aided Drug Design GRC

药物发现仍然是科学可以对公共卫生产生直接影响的最有效机制之一。在过去的30年里，人们对计算在这方面的应用越来越感兴趣-从分子系统的全原子模拟到分子信息的规范化和组织。一些方法，特别是分子信息学，现在是任何药物发现过程的基石，甚至正在成为监管的重要性。另一些则作为技术和科学研究途径仍在发展。这种研究的一个重要方面是评估进展--一种方法实际上比另一种方法更好吗？应用于药物发现的分子建模领域（计算机辅助药物设计，或CADD）的困难在于该过程非常漫长且非常昂贵。因此，相对于另一种方法严格测试一种方法通常是不可行的。例如，没有人会对两种药物发现方法进行双盲研究，直到药物批准，尽管可能会学到什么。通常最好的办法是检查现存的数据，并评估一种方法是否比另一种方法更适合观察到的情况。这就需要对统计数据进行评估。然而，在介绍和出版物中明显缺乏对可以取得的成果的了解。这是可以解决的。这次会议的计划是提高对现代统计学可能性的认识，可以说，将计算应用于计算。为此，计划采取四管齐下的办法。(1)去寻找任何研究者都可以从中获得洞察力的简单技术的介绍，这些技术比这种价值可能暗示的更不为人知。(2)征集关于CADD领域主要统计问题的工作和演讲，即在模型构建中纳入实验数据和计算过程中的错误，解决模型过度参数化的方法，评估药物化学过程中追溯性数据集的非理想性后果，以及在评估新方法时使用和选择适当的NULL模型。(3)请该领域的高级成员就如何建立统计标准发表意见，最后，（4）介绍其他学科的观点。此外，还计划举办实践会议，与会者可以使用社区数据或自带数据并获得专家的建议。希望这次会议将导致在分子建模，出版和演示标准领域的定期自我评估，并帮助推进药物设计和发现方法的严格区分。

DESCRIPTION (provided by applicant): Drug discovery remains one of the most effective mechanisms by which science can have direct impact on public health. In the last 30 years there has been ever increasing interest in the application of computation to this endeavor- ranging from the all-atom simulation of molecular systems to the canonicalization and organization of molecular information. Some approaches, in particular molecular informatics, are now cornerstones of any drug discovery process and are even becoming of regulatory importance. Others are still evolving as techniques and scientific avenues of research. An ever-important aspect of such research is the evaluation of progress - is one method actually better than another? The difficulty of the field of molecular modeling as applied to drug discovery (Computer-Aided Drug Design, or CADD) is that the process is exceedingly long and very expensive. As such, it is usually infeasible to rigorously test one method relative to another. No one is going to run, for example, double blind studies of two methods of drug discover through to drug approval, despite what might be learned. The best that can usually be hoped for is to examine extant data and evaluate whether one method fits what was observed better than another. This requires an appreciation of statistics. However, the level of knowledge of what could be achieved is noticeably absent from presentations and publications. This can be addressed. The plan for this conference is to improve awareness of what is possible with modern statistics, to apply, so to speak, computation to computation. To do this, a four-pronged approach is planned. (1) To canvas for presentations of straightforward techniques from which any investigator might gain insight and which are less well-known than such value might imply. (2) To solicit work and presentations on major statistical issues in the field of CADD, namely the incorporation of error in both experimental data and computational process in the construction of models, methods to address over- parameterization of models, the assessment of the consequences of the non-ideality of retrospective datasets derived from the medicinal chemistry process, and the use and choice of appropriate NULL models in assessing new approaches. (3) To ask for allocutions from senior members of the field as to how statistical standards might be established and, finally, (4) presentations of perspectives from other disciplines. In addition, itis planned to have hands-on sessions where attendees may work with community data or bring their own and gain advice from experts. It is hoped that this conference will lead to regular self-assessment in the field of molecular modeling, standards for publication and presentations and help advance the rigorous discrimination of approaches to drug design and discovery.