Tootfinder

Decoding the dark proteome: Deep learning-enabled discovery of druggable enzymes in Wuchereria bancrofti
Shawnak Shivakumar, Jefferson Hernandez
https://arxiv.org/abs/2510.07337

Decoding the dark proteome: Deep learning-enabled discovery of druggable enzymes in Wuchereria bancrofti
Wuchereria bancrofti, the parasitic roundworm responsible for lymphatic filariasis, permanently disables over 36 million people and places 657 million at risk across 39 countries. A major bottleneck for drug discovery is the lack of functional annotation for more than 90 percent of the W. bancrofti dark proteome, leaving many potential targets unidentified. In this work, we present a novel computational pipeline that converts W. bancrofti's unannotated amino acid sequence data into precise four…

A reduced-order derivative-informed neural operator for subsurface fluid-flow
Jeongjin (Jayjay), Park, Grant Bruer, Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Felix J. Herrmann
https://arxiv.org/abs/2509.13620

A reduced-order derivative-informed neural operator for subsurface fluid-flow
Neural operators have emerged as cost-effective surrogates for expensive fluid-flow simulators, particularly in computationally intensive tasks such as permeability inversion from time-lapse seismic data, and uncertainty quantification. In these applications, the fidelity of the surrogate's gradients with respect to system parameters is crucial, as the accuracy of downstream tasks, such as optimization and Bayesian inference, relies directly on the quality of the derivative information. Recent …