Pournami P N

The past few years have witnessed machine learning techniques take the limelight in multiple research domains. One such domain that has reaped the benefits of machine learning is computer-aided drug discovery, where the search space for candidate drug molecules is decreased using methods such as virtual screening. Current state-of-the-art sequential neural network models have shown promising results and we would like to replicate similar results with virtual screening using the encoded molecular information known as simplified molecular-input line-entry system (SMILES). Our work includes the use of attention-based sequential models — the long short-term memory with attention and an optimized version of the transformer network specifically designed to deal with SMILES (ChemBERTa). We also propose the “Overall Screening Efficacy”, an averaging metric that aggregates and encapsulates the model performance over multiple datasets. We found an overall improvement of about 27% over the benchmark model, which relied on parallelized random forests.

The registration process in the medical field has evolved rapidly making the physicians to rely on computer algorithms for processing and diagnosing the diseases. Registration has been used for studying disease growth and treatment responses of the diseases. Brain image registration enables physicians to diagnose diseases like Alzheimer's based on the changes in the internal structure of the brain. One of the main challenges in this topic is the huge computational requirements of the registration processes. In this paper we propose a method for the fast and accurate registration of MR brain images using the shape property of the axial slice of the brain. The algorithm exhibits reduced computational time when compared to a standard existing approach, which makes it useful for real time applications.