In recent years, individual and collective human intelligence, defined as the knowledge, skills, reasoning and intuition of individuals and groups, have been used in combination with computer algorithms to solve complex scientific problems. Such approach was successfully used in different research fields such as: structural biology, comparative genomics, macromolecular crystallography and RNA design. Herein we describe an attempt to use a similar approach in small-molecule drug discovery, specifically to drive search strategies of de novo drug design. This is assessed with a case study that consists of a series of public experiments in which participants had to explore the huge chemical space in silico to find predefined compounds by designing molecules and analyzing the score associate with them. Such a process may be seen as an instantaneous surrogate of the classical design-make-test cycles carried out by medicinal chemists during the drug discovery hit to lead phase but not hindered by long synthesis and testing times. We present first findings on (1) assessing human intelligence in chemical space exploration, (2) comparing individual and collective human intelligence performance in this task and (3) contrasting some human and artificial intelligence achievements in de novo drug design.

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药物设计中的个人和集体人类智慧：评估搜索策略

近年来，个人和集体的人类智能（定义为个人和群体的知识、技能、推理和直觉）已与计算机算法结合使用来解决复杂的科学问题。这种方法已成功应用于不同的研究领域，例如：结构生物学、比较基因组学、大分子晶体学和 RNA 设计。在此，我们描述了在小分子药物发现中使用类似方法的尝试，特别是推动从头药物设计的搜索策略。这是通过一个案例研究进行评估的，该案例研究由一系列公共实验组成，参与者必须在计算机中探索巨大的化学空间，通过设计分子并分析与它们相关的分数来找到预定义的化合物。这样的过程可以被看作是药物化学家在药物发现进入领先阶段期间进行的经典设计-制造-测试循环的即时替代，但不受长合成和测试时间的阻碍。我们提出了关于 (1) 在化学空间探索中评估人类智能、(2) 比较个人和集体人类智能在这项任务中的表现以及 (3) 对比从头药物设计中的一些人类和人工智能成就的初步发现。