Browsing by Author "Alshehri, Abdulelah Saeed"

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DEEP LEARNING FOR MOLECULAR DESIGN: MODELS, FRAMEWORKS, AND APPLICATIONS
(Cornell University, 2024-08) Alshehri, Abdulelah Saeed; You, Fengqi; Gomes, Carla; Abbott, Nicholas L.
The vast and complex landscape of chemical space has traditionally been explored through a combination of experimentation and knowledge-based computational approaches. However, the limitations of these methods have hindered the efficient design of molecules with desired properties. The advent of deep learning, coupled with the availability of big chemical data, presents transformative opportunities for computational molecular design. This dissertation explores the convergence of deep learning and chemical engineering, presenting novel methodologies and frameworks to address challenges in molecular property prediction, molecular design, chemical data extraction, molecular conformation generation, and peptide design. In Chapter 2, we develop parallel models for the estimation of 25 pure component properties across over 24,000 chemicals, employing both traditional regression and machine learning methods on functional group representations. These models demonstrate robust accuracy in predicting a broad range of physicochemical properties, enabling streamlined product and process design. Chapter 3 addresses the inherent uncertainty in CMD by introducing DRL-CMD, an uncertainty-aware deep reinforcement learning framework. By explicitly quantifying and managing uncertainties, DRL-CMD reduces constraint violations by 39% and uncertainty margins by 27% compared to literature-reported molecules, particularly in complex design scenarios with limited data and extreme property ranges. This approach offers a more reliable path to molecules with tailored properties toward accelerating product and process design. In Chapter 4, the focus is on the extraction of chemical data from scientific literature, critical for model training and discovery. ChemREL, a novel deep learning pipeline, achieves an F1-score of 95.4% for property extraction, outperforming existing methods and GPT-4. Its transferability is demonstrated by successful adaptation from melting point extraction to LD50 extraction with minimal additional training, highlighting the potential to accelerate the construction of large-scale chemical datasets. In Chapter 5, we explore the utilization of abundant 2D molecular graph data to enhance 3D conformer generation, a crucial step in drug discovery. By pretraining graph neural networks on 2D data and improving the GeoMol method, we achieve a 7.7% average improvement in generated conformer quality compared to state-of-the-art sequential methods, improving the accuracy and efficiency of molecular modeling. Chapter 6 addresses the global challenge of plastic pollution by presenting an integrated framework combining biophysics-based insights, evidential deep learning, and metaheuristic search for the design of plastic-binding peptides. This approach leads to significant increases in binding free energies for polypropylene (18%) and polystyrene (34%) compared to previous designs, offering a promising bio-inspired solution for plastic remediation. By developing these novel deep learning approaches, the resulting advances improve predicting molecular properties, designing molecules with tailored properties while managing uncertainties, constructing a versatile pipeline for chemical data extraction, enhancing the quality of 3D conformer generation, and generating high-affinity plastic-binding peptides for potential environmental remediation. These works signify a step forward in the integration of deep learning and chemical engineering, paving the way for accelerated discovery and innovation in the field.
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