2013 Computer-Aided Drug Design GRC

描述(由申请人提供)：药物发现仍然是科学对公众健康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，甚至变得具有监管重要性。其他的仍在发展成为研究的技术和科学途径。这类研究的一个非常重要的方面是对进展的评估--一种方法实际上比另一种方法更好吗？将分子建模应用于药物发现(计算机辅助药物设计，或CADD)领域的困难在于，这个过程非常长，而且非常昂贵。因此，严格地测试一种方法相对于另一种方法通常是不可行的。例如，没有人会对药物发现的两种方法进行双盲研究，直到药物批准，尽管可能会了解到什么。通常最好的希望是检查现有的数据，并评估一种方法是否比另一种方法更符合观察到的情况。这需要对统计数字有所了解。然而，在专题介绍和出版物中明显缺乏对可实现目标的了解程度。这是可以解决的。这次会议的计划是提高人们对现代统计学的可能性的认识，也就是说，将计算应用于计算。要做到这一点，计划采取四管齐下的方法。(1)为直截了当的技术展示画布，任何调查人员都可以从中获得洞察力，但这些技术并不像这种价值所暗示的那样广为人知。(2)就计算机辅助设计开发领域的主要统计问题征集工作和专题介绍，即在建立模型时将实验数据和计算过程中的误差纳入其中，解决模型过度参数问题的方法，评估药物化学过程中得出的追溯数据集的非理想性的后果，以及在评估新的办法时使用和选择适当的零模型。(3)就如何制定统计标准征求该领域高级成员的意见，最后，(4)介绍其他学科的观点。此外，它还计划举办实践会议，与会者可以使用社区数据，也可以自带数据并听取专家的建议。希望这次会议将导致在分子建模、出版和介绍标准领域进行定期自我评估，并有助于推动对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.