Solubility is a widely measured physicochemical property and a highly sought-after descriptor in the evaluation of molecules for drug design and development, medicinal chemistry, and agrochemical sciences. Consequently, a wide range of in silico models based on deductive and inductive reasoning have been developed. However, the General Solubility Equation (GSE) stands out as a simple, thermodynamically derived equation to predict intrinsic solubility (log S0, in mol/L) of molecules from their melting point and n-octanol/water partition coefficient (log PN). Despite its widespread use, the applicability of this equation has not been rigorously evaluated to establish when it can or cannot produce accurate predictions. Therefore, a large-scale evaluation of the GSE was conducted using an extensive database of fully experimental melting point and log PN data for 2740, mostly drug-like compounds. A systematic analysis based on cheminformatics descriptors was conducted to identify the primary factors that allow the GSE to achieve predictions within the experimental uncertainty range (±1 log S unit). This information has been used to create GSESolver: a supervised machine learning classification model that, based on the selected descriptors, can predict whether the GSE will perform accurately solely from its SMILES string. GSESolver tool can be used by anyone in the scientific community to quickly assess molecule solubility and it was developed under the FAIR principles (Findable, Accessible, Interoperable, and Reusable) in a Google Colab notebook to ensure ease of use. Finally, the utility of the GSESolver tool was exploited in medicinal chemistry applications for the in silico prediction of the Maximum Absorbable Dose (MAD) of potential drugs, and in a rational agrochemical design application where solubility is a key factor.
Esteban Bertsch-Aguilar, Nahomy Quirós, Frederick Schosinsky et al.· Journal of Chemical Informat...· 0 citations
The increasing prevalence of antimicrobial resistance has stimulated the search for new molecular scaffolds with improved efficacy and selectivity. In this study, five bile acid–derived aminosteroids (AC series) and their corresponding biphenyl‐functionalized derivatives (BIAC series) were synthesized and evaluated against Staphylococcus aureus and Enterococcus faecalis. Antimicrobial activity, hemolytic activity, and lipophilicity were investigated to establish structure–activity relationships. Quantum mechanics–derived lipophilicity (QM‐logP) successfully captured subtle differences associated with the number, position, and orientation of hydroxyl groups on the steroid nucleus, providing a level of discrimination not achieved by conventional fragment‐based methods. Introduction of the biphenyl moiety significantly increased lipophilicity and resulted in enhanced antimicrobial activity throughout the BIAC series compared with the parent AC compounds. BIAC05Q emerged as the most promising derivative, combining potent antibacterial activity with low hemolysis. The superior performance of BIAC05Q appears to be associated with the presence of an axial hydroxyl group at C7, which promotes a favorable amphiphilic balance and aggregation propensity. Collectively, the results suggest that the antimicrobial activity of these bile acid derivatives is closely linked to their supramolecular behavior and support the hypothesis that self‐assembled aggregates, rather than individual molecules, may constitute the bioactive species. These findings highlight the value of QM‐logP–guided molecular design as a strategy to optimize amphiphilicity, aggregation, antimicrobial activity, and selectivity in bile acid–based antimicrobial agents.
Allan Mora Abarca, Luis Rivera-Montero, K. Barrantes et al.· BioMed Research Internationa...· 0 citations
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