Barathi Lenin

Abstract Structure-constrained molecular generation (SCMG) generates novel molecules that are structurally similar to a given molecule and have optimized properties. Deep learning solutions for SCMG are limited in that they are pre-disposed towards existing knowledge, and they suffer from a natural impedance mismatch problem due to the discrete nature of molecules, while deep learning methods for SCMG often operate in continuous space.

Abstract Monitoring microbial components in drinking water is as essential as tracking its chemical composition. While traditional culture-based methods can offer valuable insights into the morphology, behavior, and phenotype of organisms, their scope is limited to culturable species. With the advent of high-throughput sequencing, we can now detect a wider range of microbes in any ecosystem, gaining deeper insights into their functional potential and metabolic capabilities more efficiently.