Kahini Wadhawan

TL;DR: Co-regularized domain alignment as mentioned in this paper constructs multiple diverse feature spaces and aligns source and target distributions in each of them individually, while encouraging that alignments agree with each other with regard to the class predictions on the unlabeled target examples.

TL;DR: In this article, a computational method leveraging deep learning and molecular dynamics simulations enables the rapid discovery of antimicrobial peptides with low toxicity and with high potency against diverse Gram-positive and Gram-negative pathogens.

TL;DR: Co-regularized domain alignment as mentioned in this paper constructs multiple diverse feature spaces and aligns source and target distributions in each of them individually, while encouraging that alignments agree with each other with regard to the class predictions on the unlabeled target examples.

TL;DR: QMO is proposed, a generic query-based molecule optimization framework that exploits latent embeddings from a molecule autoencoder that improves the desired properties of an input molecule based on efficient queries, guided by a set of molecular property predictions and evaluation metrics.

TL;DR: A peptide generation framework PepCVAE, based on a semi-supervised variational autoencoder (VAE) model, for designing novel antimicrobial peptide (AMP) sequences that generates novel AMP sequences with higher long-range diversity, while being closer to the training distribution of biological peptides.