Vani Kondaparthi

Pro-GAT repairs chemically disconnected PROTAC candidates generated by diffusion models, improving validity while maintaining uniqueness.

InversePep generates peptides with target structural geometries using a diffusion model, outperforming existing methods in producing geometry-consistent sequences.

MHCBind is a deep learning model that predicts MHC class I peptide binding affinity across alleles and improves upon existing methods with better accuracy and ranking performance.

OligoGraph predicts siRNA efficacy using a graph-based deep learning model, outperforming existing methods on multiple datasets for both 19- and 21-nucleotide siRNAs.

RNAiSpline predicts siRNA efficacy using deep learning with self-supervised pretraining and KAN, CNN, and Transformer Encoder, achieving high ROC-AUC and F1 scores.

The paper introduces MED, a deep learning framework for generating macrocycles from acyclic molecules, achieving high validity and binding affinity in drug discovery.

ABFormer improves ADC activity prediction by using contextualized antibody-antigen embeddings, outperforming baselines with 100% accuracy on a test set.

SiaRNA predicts siRNA-mRNA efficacy using a Siamese Neural Network with bidirectional cross-attention, outperforming existing methods on benchmark datasets.

CrossPPI predicts protein-protein binding affinity using a cross-fusion deep learning model that integrates sequence and structural features, achieving high correlation and low error metrics on a test dataset.