Winkler David A
CSIRO Manufacturing, Bayview Avenue, Clayton 3168, Australia.
Monash Institute of Pharmaceutical Sciences, 392 Royal Parade, Parkville 3052, Australia.
Beilstein J Org Chem. 2017 Jun 29;13:1288-1302. doi: 10.3762/bjoc.13.125. eCollection 2017.
A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known "S curve", with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.
生命系统的一个主要特征是它们能够通过学习和进化来适应环境变化。大自然在这方面做得非常出色，以至于现在大量的研究工作都在试图模仿生物过程。最初，这种仿生学涉及开发合成方法来生成复杂的生物活性天然产物。最近的研究试图了解分子机器是如何运作的，以便能够复制其原理，并学习如何运用仿生进化和学习方法来解决科学、医学和工程领域的复杂问题。自动化、机器人技术、人工智能和进化算法正在融合，以产生大致可称为基于计算机模拟的材料自适应进化。这些方法正被应用于有机化学，以系统化反应、创建执行单元操作的合成机器人，并设计闭环流动自优化化学合成系统。大多数科学创新和技术都要经历著名的“S曲线”，开始时进展缓慢，能力几乎呈指数增长，然后进入稳定的应用阶段。基于自适应、进化、机器学习的分子设计和优化方法正接近快速增长阶段，其影响已被描述为可能具有颠覆性。本文描述了仿生自适应、进化、学习计算分子设计方法的新进展及其在化学、工程和医学领域的潜在影响。
