COMPUTATIONAL ENZYME ENGINEERING: FROM MOLECULAR MODELING TO MACHINE LEARNING
type: CreativeWork
description: The chemical, pharmaceutical, and materials industries increasingly rely on sustainable and efficient strategies for molecular synthesis, driving the growing importance of biocatalysis in modern life sciences. Enzymes, as highly selective and efficient natural catalysts, offer significant advantages for environmentally benign and carbon-neutral processes. However, their practical application is often limited by narrow substrate scope, insufficient stability, and incomplete understanding of structure–function relationships. This PhD research addresses these challenges by developing and applying advanced computational approaches for enzyme engineering, integrating molecular modeling and machine learning, collaborating with experimental expertise to improve enzyme design, predict biocatalytic behavior, and expand catalytic capabilities. A main focus of this work is the prediction of cytochrome P450 (CYP450)-mediated metabolism and inhibition, which play critical roles in drug efficacy and toxicity. A systematic benchmarking study of machine learning models for CYP450 substrate and inhibitor prediction was conducted, followed by the development of two predictive frameworks. First, CypEGAT integrates protein embeddings with graph-based molecular representations to predict interactions across five major human CYP450 isoforms. Building on this, CypST was developed as a multimodal deep learning framework combining ESM-3 protein embeddings and MolBERT-derived chemical features through attention-based fusion strategies. CypST demonstrated improved predictive performance over existing methods across nine CYP450 isoforms. Additionally, a toxicity prediction module trained on Tox21 dataset enabled the identification of hepatotoxicity-related mechanisms, although prediction of non-hepatic toxicities remains challenging due to limited endpoint coverage. To complement data-driven approaches, this work also investigates enzyme structure–function relationships through classical molec…
abstract: The chemical, pharmaceutical, and materials industries increasingly rely on sustainable and efficient strategies for molecular synthesis, driving the growing importance of biocatalysis in modern life sciences. Enzymes, as highly selective and efficient natural catalysts, offer significant advantages for environmentally benign and carbon-neutral processes. However, their practical application is often limited by narrow substrate scope, insufficient stability, and incomplete understanding of structure–function relationships. This PhD research addresses these challenges by developing and applying advanced computational approaches for enzyme engineering, integrating molecular modeling and machine learning, collaborating with experimental expertise to improve enzyme design, predict biocatalytic behavior, and expand catalytic capabilities. A main focus of this work is the prediction of cytochrome P450 (CYP450)-mediated metabolism and inhibition, which play critical roles in drug efficacy and toxicity. A systematic benchmarking study of machine learning models for CYP450 substrate and inhibitor prediction was conducted, followed by the development of two predictive frameworks. First, CypEGAT integrates protein embeddings with graph-based molecular representations to predict interactions across five major human CYP450 isoforms. Building on this, CypST was developed as a multimodal deep learning framework combining ESM-3 protein embeddings and MolBERT-derived chemical features through attention-based fusion strategies. CypST demonstrated improved predictive performance over existing methods across nine CYP450 isoforms. Additionally, a toxicity prediction module trained on Tox21 dataset enabled the identification of hepatotoxicity-related mechanisms, although prediction of non-hepatic toxicities remains challenging due to limited endpoint coverage. To complement data-driven approaches, this work also investigates enzyme structure–function relationships through classical molec…
author: WEI, YAO
language: eng
keywords: Settore BIOS-07/A - Biochimica
date published: 2026-05-14
date modified: 2026-07-27T20:18:53Z
provider: Università degli Studi di Milano
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Università degli Studi di Milano

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2434/1244869