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Meeting	MS&T23: Materials Science & Technology
Symposium	Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
Presentation Title	AI/ML Aided Drug Biomolecule and Materials Design
Author(s)	Mehdi Yazdani-Jahromi, Ali Khodabandeh Yalabadi, Aida Tayebi, Niloofar Yousefi, Elayaraja Kolanthai, Craig J. Neal, Sudipta Seal, Ozlem Ozmen Garibay
On-Site Speaker (Planned)	Mehdi Yazdani-Jahromi
Abstract Scope	In-silico virtual screening approaches can be time-consuming and expensive. Artificial intelligence has been widely applied across different stages of drug biomolecule design and development and has significantly reduced the time and cost involved in the process. In our previous study, we proposed BindingSite-AugmentedDTA. This framework is highly generalizable, interpretable, and can be combined with any deep learning-based regression model and can improve Drug-Target Affinity (DTA) predictions by finding the most probable binding sites of protein and narrowing down the search space. Our computational results show performance improvement in the seven state-of-the-art DTA models by up to 20%. Furthermore, 13 compounds were used as experimental validation. AI/ML-designed biomolecules are added to nano-surfaces for pathogen mitigation. These measurements were in high agreement with computationally predicted results and supported the claim that Utilizing this framework as a tool has the potential to accelerate drug discovery and other biomaterials design processes.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accurate Prediction of Oxygen Vacancy Concentration with Disordered A-site Cations in High-entropy Perovskite Oxides

AI/ML Aided Drug Biomolecule and Materials Design

Autonomous Learning of Phase Trajectories via Physics-inspired Graph Neural Networks

B-1: Multi-objective Optimization for Improving Mechanical Properties of Aluminum Alloys: A Data Analytics Approach with Machine Learning and Genetic Algorithms

B-2: Simple Data Analytics Approach Coupled with Physics-based Model for Improved Prediction of Creep Rupture Life

Computing Grain Boundary "Phase" Diagrams: From Thermodynamic Models and Atomistic Simulations to Machine Learning

Development of Machine Learning Interatomic Potentials to Model Materials Processing & Performance in Multicomponent Systems

High-throughput, Ultra-fast Laser Fabrication of Alumina sample arrays and Deep-learning Based Prediction of Realistic Alumina Microstructure from Hardness

Machine Learning-assisted Exploration of the Chemistry-processing Design Space Under Additive Manufacturing: Application to an FCC HEA Space

Machine Learning-based Prediction of the Mechanical Properties of Microalloyed Steel Subjected to Thermomechanical Controlled Processing

Machine Learning for Phase Prediction of High-entropy Alloys Assisted by Imbalance Learning

Online Mechanical Properties Control for Steel Coils Using Machine Learning Model

Optical and Photothermal Property Prediction of Gold Nanoparticle/polymer Hybrid Films Through Machine Learning and Finite Element Modeling

Optimizing Heat Treatment Routes for Ni-based Alloys Using Monte Carlo Tree Search

Physics-informed Machine Learning for Crystal Plasticity Model Calibration of Ti-7Al Alloy

Prediction of the Mechanical Response of Zirconia-reinforced Metal-matrix Composite Using Deep Learning Approaches

Process Cycle Modeling with AI

Representation, Regeneration and Prediction of Microstructure in Additive Friction Stirring via Deep Regeneration Neural Network

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