A Patient-Level Data Meta-analysis of the Abscopal Effect
Advances in Radiation Oncology, 2022
A Patient-Level Data Meta-analysis of the Abscopal Effect
Advances in Radiation Oncology, 2022
A Patient-Level Data Meta-analysis of the Abscopal Effect
Advances in Radiation Oncology, 2022
NeuralDock: Rapid and conformation-agnostic docking of small molecules
ABSTRACTVirtual screening is a cost- and time-effective alternative to traditional high-throughpu... more ABSTRACTVirtual screening is a cost- and time-effective alternative to traditional high-throughput screening in the drug discovery process. Both virtual screening approaches, structure-based molecular docking and ligand-based cheminformatics, suffer from computational cost, low accuracy, and/or reliance on prior knowledge of a ligand that binds to a given target. Here, we propose a neural network framework, NeuralDock which accelerates the process of high-quality computational docking by a factor of 106, and does not require prior knowledge of a ligand that binds to a given target. By approximating both protein-small molecule conformational sampling and energy-based scoring, NeuralDock accurately predicts the binding energy and affinity of a protein-small molecule pair, based on protein pocket 3D structure and small molecule topology. We use NeuralDock and 25 GPUs to dock 937 million molecules from the ZINC database against superoxide dismutase-1 in 21 hours, which we validate with ...
Many real-world problems rely on finding eigenvalues and eigenvectors of a matrix. The power iter... more Many real-world problems rely on finding eigenvalues and eigenvectors of a matrix. The power iteration algorithm is a simple method for determining the largest eigenvalue and associated eigenvector of a general matrix. This algorithm relies on the idea that repeated multiplication of a randomly chosen vector x by the matrix A gradually amplifies the component of the vector along the eigenvector of the largest eigenvalue of A while suppressing all other components. Unfortunately, the power iteration algorithm may demonstrate slow convergence. In this report, we demonstrate an exponential speed up in convergence of the power iteration algorithm with only a polynomial increase in computation by taking advantage of the commutativity of matrix multiplication.
Traditional drug discovery pipeline takes several years and cost billions of dollars. Deep genera... more Traditional drug discovery pipeline takes several years and cost billions of dollars. Deep generative and discriminative models are widely adopted to assist in drug development. Classical machines cannot efficiently produce atypical patterns of quantum computers which might improve the training quality of learning tasks. We propose a suite of quantum machine learning techniques e.g., generative adversarial network (GAN), convolutional neural network (CNN) and variational auto-encoder (VAE) to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules, respectively.
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Papers by Congzhou Sha