Author
ORCID 0009-0002-2926-3659Unclaimed ORCID profile · follow to get notified of new preprints
6 papers
Recent·Most cited
Saahithi Vemuri, Laxmi Priya Bijigiri, Sanjana Gogte, Vani Kondaparthi
Pro-GAT repairs chemically disconnected PROTAC candidates generated by diffusion models, improving validity while maintaining uniqueness.
Srinivas Kashyap Chilakamarri, Sneha Reddy Kasturi, Sai Pranav Reddy Yerrabandla…·Vani Kondaparthi
InversePep generates peptides with target structural geometries using a diffusion model, outperforming existing methods in producing geometry-consistent sequences.
Navyatha Peddi, Dharani Reddy Bijjula, Sanjana Gogte, Vani Kondaparthi
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.
Sai Shobit Kambampati, Siri Anumandla, Sai Lahari Guttula…·Vani Kondaparthi
The paper introduces MED, a deep learning framework for generating macrocycles from acyclic molecules, achieving high validity and binding affinity in drug discovery.
Rushi Katabathuni, Viren Loka, Sanjana Gogte, Vani Kondaparthi
ABFormer improves ADC activity prediction by using contextualized antibody-antigen embeddings, outperforming baselines with 100% accuracy on a test set.
Suhas Reddy Singam, Nithish Chandra Azad Devarashetty, Sanjana Gogte, Vani Kondaparthi
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.