Application, evaluation, and improvement of computational methodologies in drug discovery

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of “Computer-Aided Drug Design” (CADD), and they now constitute one of the pillars in the drug discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing to proficiently and rationally choose proper compounds for a desired therapeutic need among the extreme vastity of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. As a result of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can be also obtained with other computer-based techniques. In this Ph.D. work, we extensively applied state-of-the-art Computer-Aided Drug Design methods in different scenarios of pharmaceutical and biological interest, highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work combines the practical implementation of computational approaches with the development of their methods, about which we focused mainly on the field of novel filtration and scoring methods for candidate selection.