id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-103528-3tib5o1m	Ahmed, Asad	DEELIG: A Deep Learning-based approach to predict protein-ligand binding affinity	2020-09-28		.txt	text/plain	3798	242	54	title: DEELIG: A Deep Learning-based approach to predict protein-ligand binding affinity Here, we have incorporated Convolutional Neural Networks that find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies towards a diverse set of ligands. Initial raw data database created contained protein structures in PDB format, protein sequences in FASTA format, ligand in SDF format and binding affinity values of corresponding protein-ligand pairs for 5464 complexes. We propose a deep-learning based approach to predict ligand (eg., drug)-target binding affinity using only structures of target protein (PDB format) and ligand (SDF format) as inputs. We have trained two models to predict the binding affinity between protein and ligand in a given complex. We have constructed a novel dataset that represents a diverse set of ligands and using a novel deep learning based approach we have achieved significant improvement in prediction of binding affinity of protein-ligand complexes.	./cache/cord-103528-3tib5o1m.txt	./txt/cord-103528-3tib5o1m.txt