Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

https://www.mdpi.com/1422-0067/20/18/4331/pdf

Molecular Docking: Shifting Paradigms in Drug Discovery.

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure–activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

QSAR without borders

Objectives Drug repositioning, that is, the use of a drug in an indication other than the one for which it was initially marketed, is a growing trend. Its origins lie mainly in the attrition experienced in recent years in the field of new drug discovery. Key findings Despite some regulatory and economic challenges, drug repositioning offers many advantages, and a number of recent successes have confirmed both its public health benefits and its commercial value. The first examples of successful drug repositioning mainly came about through serendipity like acetylsalicylic acid, thalidomide, sildenafil or dimethylfumarate. Conclusion The history of great-repositioned drugs has given some solutions to various pathologies. Serendipity is not yet useful to find repositioning drugs. Drug repositioning is of growing interest. Nowadays, a more rational approach to the identification of drug candidates for repositioning is possible, especially using data mining.

/pdf/drug-repositioning-a-brief-overview-56w298iy23.pdf

Drug repositioning: a brief overview.

Antimicrobial resistance continues to be a public threat on a global scale. The ongoing need to develop new antimicrobial drugs that are effective against multi-drug-resistant pathogens has spurred the research community to invest in various drug discovery strategies, one of which is drug repurposing—the process of finding new uses for existing drugs. While still nascent in the antimicrobial field, the approach is gaining traction in both the public and private sector. While the approach has particular promise in fast-tracking compounds into clinical studies, it nevertheless has substantial obstacles to success. This Review covers the art of repurposing existing drugs for antimicrobial purposes. We discuss enabling screening platforms for antimicrobial discovery and present encouraging findings of novel antimicrobial therapeutic strategies. Also covered are general advantages of repurposing over de novo drug development and challenges of the strategy, including scientific, intellectual property and regulatory issues. This Review describes the potential opportunities for finding new uses as antimicrobials for existing drugs, the approaches used for screening and the scientific, intellectual property and regulatory challenges to be overcome.

Drug repurposing for antimicrobial discovery

Drug repurposing has become an important branch of drug discovery Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications A number of successful exam-ples are now available Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug-target, target-disease, and ulti-mately drug-disease associations

/pdf/on-the-integration-of-in-silico-drug-design-methods-for-drug-2mbjcnzfw7.pdf

On the integration of in silico drug design methods for drug repurposing

Usage of model combination in computational toxicology.

There is a widely recognized need to reduce human activity's impact on the environment. Many industries of the leather and textile sector (LTI), being aware of producing a significant amount of residues (Keßler et al. 2021; Liu et al. 2021), are adopting measures to reduce the impact of their processes on the environment, starting with a more comprehensive characterization of the chemical risk associated with the substances commonly used in LTI. The present work contributes to these efforts by compiling and toxicologically annotating the substances used in LTI, supporting a continuous learning strategy for characterizing their chemical safety. This strategy combines data collection from public sources, experimental methods and in silico predictions for characterizing four different endpoints: CMR, ED, PBT, and vPvB. We present the results of a prospective validation exercise in which we confirm that in silico methods can produce reasonably good hazard estimations and fill knowledge gaps in the LTI chemical space. The proposed protocol can speed the process and optimize the use of resources including the lives of experimental animals, contributing to identifying potentially harmful substances and their possible replacement by safer alternatives, thus reducing the environmental footprint and impact on human health. 

/pdf/a-continuous-in-silico-learning-strategy-to-identify-safety-hwyo1ag7.pdf

A continuous in silico learning strategy to identify safety liabilities in compounds used in the leather and textile industry

Moving towards making (quantitative) structure-activity relationships ((Q)SARs) for toxicity-related endpoints findable, accessible, interoperable and reusable (FAIR).

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On the integration of in silico drug design methods for drug repurposing

Usage of model combination in computational toxicology.

A continuous in silico learning strategy to identify safety liabilities in compounds used in the leather and textile industry

Moving towards making (quantitative) structure-activity relationships ((Q)SARs) for toxicity-related endpoints findable, accessible, interoperable and reusable (FAIR).