id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-352886-6lzlt6ur	Bai, Qifeng	MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm	2020-08-11		.txt	text/plain	6655	377	51	Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. To use the merit of deep learning, our designed soft tool employs the sequencebased generative model and graph neural networks (GNNs) generative model for producing the ligand set and small molecular fragments (see Figure 1 ). Figure 7B shows the drug virtual screening results of SARS-CoV-2 M pro from 2 million druglike ligands by deep learning generative model and molecular docking.	./cache/cord-352886-6lzlt6ur.txt	./txt/cord-352886-6lzlt6ur.txt