Current Trends and Challenges in Drug-Likeness Prediction: Are They Generalizable and Interpretable?

Abstract

Importance : Drug-likeness of a compound is an overall assessment of its potential to succeed in clinical trials, and is essential for economizing research expenditures by filtering compounds with unfavorable properties and poor development potential. To this end, a robust drug-likeness prediction method is indispensable. Various approaches, including discriminative rules, statistical models, and machine learning models, have been developed to predict drug-likeness based on physiochemical properties and structural features. Notably, recent advancements in novel deep learning techniques have significantly advanced drug-likeness prediction, especially in classification performance. Highlights : In this review, we addressed the evolving landscape of drug-likeness prediction, with emphasis on methods employing novel deep learning techniques, and highlighted the current challenges in drug-likeness prediction, specifically regarding the aspects of generalization and interpretability. Moreover, we explored potential remedies and outlined promising avenues for future research. Conclusion : Despite the hurdles of generalization and interpretability, novel deep learning techniques have great potential in drug-likeness prediction and are worthy of further research efforts.

1,2,3-Triazol-5-ylidene- vs. 1,2,3-triazole-based tricarbonylrhenium(i) complexes: influence of a mesoionic carbene ligand on the electronic and biological properties

“…A connection between the antibacterial activity of all complexes and common drug likeliness parameters ( i.e. topological polar surface area (TPSA), lipophilicity (miLogP) and molecular weight (MW)) 54,55 was sought. These parameters are typically determined for eukaryotic cells, but can be used to some extent for bacteria.…”