In Silico Design of Bioactive Molecules to Counteract Infectious Diseases and Neurological Disorders Introducing Environmental Impact Criteria

This thesis presents the application of ligand-based and structure-based in silico approaches for the development of predictive models and the discovery of bioactive molecules addressing environmental and medicinal chemistry challenges. The research focuses on the dual goals of predicting environmental fate endpoints of chemicals and active pharmaceutical ingredients (APIs) and designing novel compounds targeting infectious diseases and neurological disorders. The environmental dimension of Pharmaceuticals in the Environment (PiE) is addressed, highlighting the main sources of pharmaceutical residues, their ecological impact, and strategies for mitigation. The work emphasizes the integration of environmental considerations early in drug design, promoting a “Benign by Design” approach, and demonstrates the potential of computational methods to support sustainable drug development. The theoretical background encompasses Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) modeling, chemoinformatics techniques, and relevant statistical tools. Predictive models were developed to estimate key environmental fate properties, including bioconcentration factor, soil organic carbon adsorption, and ready biodegradability. The study compares 2D established modeling approaches, such as EPI Suite programs, OPERA tool and circular fingerprint-based models, with 3D VolSurf+ descriptors in combination with PLS statistics, assessing performance, interpretability, and applicability to small molecule APIs. Special attention is given to optimizing the chemical domain and enhancing a balanced predictivity within the drug-like chemical space, providing insights for environmentally conscious drug design. The medicinal chemistry component of the research focuses on two main projects: the optimization of metallo-beta-lactamase inhibitors and the design of molecules targeting Alzheimer’s disease-related proteins. Computational studies, including docking and molecular dynamics simulations, guided the design and rationalization of experimental results. In the first project, scaffold modifications of 1,2,4-triazole-3-thione derivatives were computationally explored, with subsequent synthesis, inhibition assays, and X-ray studies validating the predicted structure-activity relationships. The second project involved the design of PROTACs for the degradation of Glycogen Synthase Kinase 3-beta (GSK-3β) and the optimization of dual inhibitors targeting GSK-3β and Histone DeACetylases (HDACs), supporting the discovery of therapeutically relevant molecules. Overall, the thesis demonstrates the synergy between computational modeling and experimental validation in both environmental and medicinal chemistry contexts. The developed QSPR models offer improved balanced predictivity for pharmaceutical compounds, supporting environmentally informed drug design, while the drug discovery studies provide new insights into inhibitor optimization for critical therapeutic targets. The work highlights the transformative potential of in silico methods in addressing complex chemical and biological challenges, paving the way for future applications in sustainable pharmaceutical development and rational drug discovery.