SC²S Colloquium - March 9, 2016

Date:	March 9, 2016
Room:	02.07.023
Time:	3:00 pm, s.t.

Vinod Rajendran: Deep Learning for the Prediction of Molecular Properties

The accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient towards rational compound design in chemical and pharmaceutical industries. Since the recent past, Machine Learning (ML) techniques applied to ab initio calculations i.e. nuclear charges and Cartesian coordinates of all atoms, have been proposed as an efficient approach for predicting quantum-mechanical molecular properties directly from the raw molecular geometry. This dexterous approach results from a combination of multiple factors such as the choice of ML model and an appropriate representation of the physical properties and invariance structure of molecules. In this work, a number of well established deep learning methods have been evaluated for predicting the molecular properties. In addition, the influence of these methods on various molecular representations are examined. The results of this evaluation show that deep learning methods can be successfully applied for accelerating the chemical accuracy of molecular properties. The application of deep learning methods on quantum-chemical systems especially for the purpose of molecular properties prediction, is a general direction that is well worth for pursuing a future research.