Perceptive On Artificial Intelligence In Chemic Molecular Design

S Ravichandran; S. Brindha; Akrati Shrivastava

S Ravichandran Professor in Chemistry, School of Mechanical Engineering, Lovely Professional University, Jalandhar, Punjab
S. Brindha Assistant Professor, Department of Computer Applications, SRM Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Potheri, Chengalpattu, Tamilnadu.
Akrati Shrivastava B.Sc. Student in Agriculture, School of Agriculture, Lovely Professional University, Jalandhar, Punjab.

Abstract

The need for custom compounds is growing, which makes molecular design work challenging. To be computationally manageable, earlier approaches to molecular design relied on simplified thermodynamic models. The most thorough molecular picture is provided by quantum mechanics in comparison, but it is difficult to directly incorporate it into computer-aided molecular design (CAMD). The current use of artificial intelligence to create an automated molecular design in chemistry has been met with skepticism on a number fo fronts, but deep learning and machine learning approaches also increase conceptual, technical, scalability and end to end error quantification issues. This article seeks to identify the most current and innovative technological advancements made by each of the parts of such an autonomous artificial intelligence and machine learning process. Furthermore, it can be integrated to significantly speed up the protein target.

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