Tirtharaj Dash

Abstract Introduction: Accurate prediction of the bearing capacity of double shear-bolted connections in structural steel is essential for ensuring safety and efficiency in structural design. This study explores the application of ten machine learning algorithms to enhance prediction accuracy while addressing the interpretability challenges often associated with such models. Methods: Models were tuned with 10-fold crossvalidation and assessed using RMSE, R2 and a20 accuracy index.

Abstract Our interest is in the generation of "lead" molecules in early-stage drug design. Leads are small molecules (ligands) that can bind to a part of pre-specified target and also satisfy multiple physico-chemical constraints. We propose using techniques developed in Inductive Logic Programming (ILP) to identify a logical specification of feasible molecules; and then using this specification to construct a program that uses a large language model (LLM) to generate new molecules.

Abstract Motivation: Gene regulation involves complex interactions between multiple transcription factors. While early attempts to train deep neural networks to predict gene expression were limited to naturally occurring promoter sequences, the advent of gigantic parallel reporter assays has expanded available training data by orders of magnitude. Despite these advances, a clear understanding of how to use deep learning to study gene regulation is still lacking.