PREDICTION OF CYTOCHROME P450-RELATED DRUG-DRUG INTERACTIONS BY DEEP LEARNING

Drug-drug interactions (DDIs) occur when multiple drugs are used concurrently. Caused by one drug inhibiting or inducing the metabolism of a second drug, DDIs often alter plasma concentrations and could seriously impact efficacy and safety of co-administered medications. Cytochrome P450 (CYP), a superfamily of enzymes, plays an important role in metabolizing a majority of FDA approved drugs currently on the market. 70% of predicable DDIs are associated with CYP enzymes inhibition. In-silico methods are increasingly adopted as a cost-effective complement to guide and prioritize efforts in drug discovery. Recent emerging applications of artificial intelligence algorithms have demonstrated promising results capable of prioritizing the selection of large chemical libraries, thereby outlining the future of in-silico methods assisting in drug discovery. Nevertheless, current methods rely on molecular descriptors that almost exclusively focus on chemical properties and atomic structures that fail to capture critical conformation and biological interaction related properties. There is also a lack of trainable molecular descriptors with feature specificity that reflect detailed protein-ligand binding energy and enable biological activity prediction. The overall objective of this dissertation is to understand molecular biological binding activity through electronic structure-based local descriptors derived from quantum based conceptual density functional theory (CDFT). This method will be used to assess the correlation of intermolecular interaction energy with ligand-protein binding with 2D feature maps reduced from the 4D molecular surfaces of the binding site and ligand (3D molecular surface with 1D electronic property). Additionally, it will be used to explore the possibility of predicting CYP related DDIs using descriptors generated using first principles including protein-ligand binding with specificity and strength and deep learning algorithms. Using quantum chemistry to interpret topological molecular information residing on 3D molecular surface permits the extraction of interacting features directly from the ligand structure. To achieve that, a set of curatable data containing consistent measurements was accessed through publicly accessible libraries. A series of novel Manifold Embedding of Molecular Surface (MEMS) descriptors were generated containing local electronic properties residing on the 3D molecule structure surface of each ligand using manifold learning. Major information were captured featuring electronic characteristics on the molecular 3D surface. Shape context was employed to derive transnational invariance feature vectors from MEMS with high granularity, thus preserving molecular information with specificity. DeepSet was utilized to perform permutation equivariance model training and validation. Powerful model learning is observed with an F-measure for all targets above 75% with the highest of 87% from external testing. Despite their promising prediction performance, molecular conformation changes and analytical featurization methods need to be implemented to expand model applicability and improve model reliability.