It's was best video of molecular docking studies for beginners but it would be more better we u use some further docking process. Like use of open Babel and all
WOW, thank you! But I'm wondering where does Machine Learning take place in the procedure? Is the docking softwares built using ML? I'm planning to use Machine Learning for my research work, so in that case I wouldn't need a software or what? I'd appreciate your answer.
This area completely deals with drug designing purpose. There are specified softwares like autodock, Schrodinger etc.. to do such works. Yup Machine learning becomes an important tool to make such advancements in research. We appreciate your interest in taking good step ahead towards Machine learning. All the best 😊
Receptors & ligands are often named with the protein ( receptor) labels only. So, if you identify or choose a protein you may identify the receptor from protein data bank. Eg. If progesterone is a ligand. It's receptor is identified as simply progesterone receptor which you can get details from pdb.
@Laiba Iqbal ya sure. We have totally 5 videos on molecular docking. You can go through the videos. Which will explain the concept of docking clearly. If any further doubts you are welcomed to clarify with us in the comment section below each video or you can reach by our mail. scienceauratech@gmail.com
ADT detects whether the ligand already has charges or not. Note: you must always add hydrogens to the ligand before you select it to be the ligand. standard set of the 20 commonly occurring amino acids. If all the residues are amino acids, ADT adds Kollman charges to the ligand.
Please find below some useful references: Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998). "Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function". Journal of Computational Chemistry 19 (14): 1639-1662. • Morris RJ, Najmanovich RJ, Kahraman A, Thornton JM (May 2005). "Real spherical harmonic expansion coefficients as 3D shape descriptors for protein binding pocket and ligand comparisons".Bioinformatics 21 (10): 2347-55. • Kahraman A, Morris RJ, Laskowski RA, Thornton JM (April 2007). "Shape variation in protein binding pockets and their ligands". J. Mol. Biol. 368 (1): 283-301. • Suresh PS, Kumar A, Kumar R, Singh VP (January 2008). "An in silico [correction of insilico] approach to bioremediation: laccase as a case study". J. Mol. Graph. Model. 26 (5): 845-9. • www.ncbi.nlm.nih.gov/pubmed/18446297
