Chiroptical properties of bioactive molecules: sensitivity to conformation and solvation

Chiroptical spectroscopies play a fundamental role in pharmaceutical analysis for the stereochemical characterisation of bioactive molecules, due to the close relationship between chirality and optical activity and the increasing evidence of stereoselectivity in the pharmacological and toxicological profiles of chiral drugs. The correlation between chiroptical properties and absolute stereochemistry, however, requires the development of accurate and reliable theoretical models. The present thesis will report the application of theoretical chiroptical spectroscopies in the field of drug analysis, with particular emphasis on the huge influence of conformational flexibility and solvation on chiroptical properties and on the main computational strategies available to describe their effects by means of electronic circular dichroism (ECD) spectroscopy and time-dependent density functional theory (TD-DFT) calculations. The combination of experimental chiroptical spectroscopies with state-of-the-art computational methods proved to be very efficient at predicting the absolute configuration of a wide range of bioactive molecules (fluorinated 2-arylpropionic acids, β-lactam derivatives, difenoconazole, fenoterol, mycoleptones, austdiol). The results obtained for the investigated systems showed that great care must be taken in describing the molecular system in the most accurate fashion, since chiroptical properties are very sensitive to small electronic and conformational perturbations. In the future, the improvement of theoretical models and methods, such as ab initio molecular dynamics, will benefit pharmaceutical analysis in the investigation of non-trivial effects on the chiroptical properties of solvated systems and in the characterisation of the stereochemistry of complex chiral drugs.

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.