Abstract

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.

Highlights

  • In the last decade, different citizen science initiatives have been promoted to solve complex scientific problems using crowdsourcing and gamification [1,2,3]

  • In the field of small-molecule drug discovery a problem of this type is represented by the drug design process

  • Experiment settings and circumstances The case study3 consisted of a series of public experiments where each participant should find a specific, predefined target molecule in the chemical space

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Summary

Introduction

Different citizen science initiatives have been promoted to solve complex scientific problems using crowdsourcing and gamification [1,2,3]. The most known projects of this type, developed as on-line video games, are: Foldit, Phylo, CrowdPhase, Udock and EteRNA. Foldit predicts protein structures [4,5,6,7] and deals with de novo protein design [8]; Phylo [9] answers multiple sequence alignment questions of comparative genomics; CrowdPhase [10, 11] addresses ab initio phasing issues of macromolecular crystallography; Udock [12, 13] tackles protein–protein docking puzzles. Designing an ideal drug corresponds to finding an optimal molecule in the chemical space. This is an extremely hard task inter alia because the chemical space is huge and finding a specific molecule therein is a needle-in-a-haystack problem

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