Department of Pharmacology & Toxicology, College of Pharmacy and BIO5 Institute, University of Arizona, Tucson, USA.
J Comput Aided Mol Des. 2011 Aug;25(8):699-708. doi: 10.1007/s10822-011-9447-8. Epub 2011 Jun 23.
Reductionism is alive and well in drug-discovery research. In that tradition, we continually improve experimental and computational methods for studying smaller and smaller aspects of biological systems. Although significant improvements continue to be made, are our efforts too narrowly focused? Suppose all error could be removed from these methods, would we then understand biological systems sufficiently well to design effective drugs? Currently, almost all drug research focuses on single targets. Should the process be expanded to include multiple targets? Recent efforts in this direction have lead to the emerging field of polypharmacology. This appears to be a move in the right direction, but how much polypharmacology is enough? As the complexity of the processes underlying polypharmacology increase will we be able to understand them and their inter-relationships? Is "new" mathematics unfamiliar in much of physics and chemistry research needed to accomplish this task? A number of these questions will be addressed in this paper, which focuses on issues and questions not answers to the drug-discovery conundrum.
在药物发现研究中，还原论仍然盛行。在这一传统中，我们不断改进用于研究生物系统更小方面的实验和计算方法。尽管仍在持续取得重大进展，但我们的努力是否过于狭隘？假设这些方法可以消除所有误差，我们是否就能充分了解生物系统从而设计出有效的药物？目前，几乎所有药物研究都集中在单一靶点上。这一过程是否应该扩展到包括多个靶点？最近在这方面的努力已经催生了多靶药理学这一新兴领域。这似乎是朝着正确方向迈出的一步，但多靶药理学要达到多少才算足够？随着多靶药理学背后过程的复杂性增加，我们是否能够理解它们及其相互关系？在完成这项任务时，是否需要物理学和化学研究中不熟悉的“新”数学？本文将重点讨论药物发现难题的一些未解决的问题和疑问，以回答其中的一些问题。
