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Introduction: Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology., Areas Covered: This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples., Expert Opinion: Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Patents tend to define a huge chemical space described by the combinatorial nature of Markush structures. However, the optimization of new principal active ingredient is frequently driven by a simple Free Wilson approach. This procedure leads to a highly focused study on the chemical space near a hit compound leaving many unexplored regions that may present highly biological active reservoirs. This study aims to demonstrate that this unveiled chemical space can hide compounds with interesting potential biological activity that would be worth pursuing. This underlines the value and necessity of broadening an approach beyond conventional strategies. Hence, we advocate for an alternative methodology that may be more efficient in the early drug discovery stages. We have selected the case of Tafenoquine, a single-dose treatment for the radical cure of P. vivax malaria approved by the FDA in 2018, as an example to illustrate the process. Through the deep exploration of the Tafenoquine chemical space, seven compounds with potential antimalarial activity have been rationally identified and synthesized. This small set is representative of the chemical diversity unexplored by the 58 analogs reported to date. After biological assessment, results evidence that our approach for rational design has proven to be a very efficient exploratory methodology suitable for the early drug discovery stages., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Most of the product patents claim a large number of compounds based on a Markush structure. However, the identification and optimization of new principal active ingredients is frequently driven by a simple Free Wilson approach, leading to a highly focused study only involving the chemical space nearby a hit compound. This fact raises the question: do the tested compounds described in patents really reflect the full molecular diversity described in the Markush structure? In this study, we contrast the performance of rational selection to conventional approaches in seven real-case patents, assessing their ability to describe the patent's chemical space. Results demonstrate that the integration of computer-aided library selection methods in the early stages of the drug discovery process would boost the identification of new potential hits across the chemical space.