Dynamic applicability domain (dAD): compound-target binding affinity estimates with local conformal prediction

Validation guidelines for drug-target prediction methods

INTRODUCTION

Mapping the interactions between pharmaceutical compounds and their molecular targets is a fundamental aspect of drug discovery and repurposing. Drug-target interactions are important for elucidating mechanisms of action and optimizing drug efficacy and safety profiles. Several computational methods have been developed to systematically predict drug-target interactions. However, computational and experimental validation of the drug-target predictions greatly vary across the studies.

AREAS COVERED

Through a PubMed query, a corpus comprising 3,286 articles on drug-target interaction prediction published within the past decade was covered. Natural language processing was used for automated abstract classification to study the evolution of computational methods, validation strategies and performance assessment metrics in the 3,286 articles. Additionally, a manual analysis of 259 studies that performed experimental validation of computational predictions revealed prevalent experimental protocols.

EXPERT OPINION

Starting from 2014, there has been a noticeable increase in articles focusing on drug-target interaction prediction. Docking and regression stands out as the most commonly used techniques among computational methods, and cross-validation is frequently employed as the computational validation strategy. Testing the predictions using multiple, orthogonal validation strategies is recommended and should be reported for the specific target prediction applications. Experimental validation remains relatively rare and should be performed more routinely to evaluate biological relevance of predictions.

Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions

Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.

Rethinking the applicability domain analysis in QSAR models

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.