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[Display omitted] A rapid and reliable evaluation of the aqueous solubility of small molecules is a hot topic for the scientific community and represents a field of particular interest in drug discovery. In fact, aqueous solubility significantly impacts various aspects that collectively influence a drug's overall pharmacokinetics, including absorption, distribution and metabolism. For this reason, in silico approaches that provide fast and cost-effective solubility predictions, can serve as invaluable tools in the early stages of drug development. Although additional molecular features should be considered, accurate solubility predictions can help medicinal chemists rationally planning the synthesis of compounds more likely to exhibit desirable pharmacokinetic properties and in selecting the most promising candidates for further biological testing (e.g., cellular assays) from an initial pool of hit compounds with detected preliminary bioactivity. In this context, we herein report the development and evaluation of WaSPred, our AI-based water solubility predictor for small molecules. WaSPred not only showed high reliability in water solubility predictions performed on structurally heterogeneous compounds, belonging to multiple external datasets, but also demonstrated superior performance compared to a set of other commonly used water solubility predictors, thus confirming its state-of the-art robustness and its usefulness as an in silico approach for water solubility evaluations.. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence and machine learning (ML) methods is becoming increasingly popular in computational toxicology and drug design; it is considered as a promising solution for assessing the safety profile of compounds, particularly in lead optimization and ADMET studies, and to meet the principles of the 3Rs, which calls for the replacement, reduction, and refinement of animal testing. In this context, we herein present the development of VenomPred 2.0 (http://www.mmvsl.it/wp/venompred2/), the new and improved version of our free of charge web tool for toxicological predictions, which now represents a powerful web-based platform for multifaceted and human-interpretable in silico toxicity profiling of chemicals. VenomPred 2.0 presents an extended set of toxicity endpoints (androgenicity, skin irritation, eye irritation, and acute oral toxicity, in addition to the already available carcinogenicity, mutagenicity, hepatotoxicity, and estrogenicity) that can be evaluated through an exhaustive consensus prediction strategy based on multiple ML models. Moreover, we also implemented a new utility based on the Shapley Additive exPlanations (SHAP) method that allows human interpretable toxicological profiling of small molecules, highlighting the features that strongly contribute to the toxicological predictions in order to derive structural toxicophores.