Kapil Devkota

PRECISE improves virtual screening by combining quantized drug representations with 3D target structures to predict binding sites and enable billion-scale screening.

The paper designs immunogens that redirect immune responses to a conserved influenza epitope using iterative machine learning and in vivo testing.

This paper introduces CoNCISE, a method that accelerates drug-target interaction prediction by compressing small molecule data into 15 bits per compound, achieving high accuracy and enabling rapid screening of large chemical libraries.

PHILHARMONIC infers protein interaction networks and functional organization in non-model organisms using deep learning and spectral clustering, validated in Drosophila and coral symbionts.

Allo-Allo predicts allosteric sites using a data-efficient sequence-based method, achieving higher performance than existing approaches and showing translational potential in disease risk association.

Raygun is a generative AI framework that miniaturizes, modifies, and expands proteins while preserving function and structural integrity, validated through cell-based experiments.

This paper introduces a faster approximation of the IsoRank method for aligning large biological networks, achieving comparable accuracy with significantly reduced computational time.

The paper evaluates methods for imputing drug connectivity mapping with incomplete data, finding that neighborhood-based collaborative filtering performs best when cell type is considered.