Computer aided molecular design coupled with molecular dynamics and deep learning techniques as a novel approach to design new compounds

Abstract

In this work is proposed a novel approach to develop new compounds, through computer aided molecular design (CAMD) using group contribution methodologies for estimation of properties, follow of machine learning or molecular simulations techniques for estimation and validation of the target properties of the design; finally, Monte Carlo simulations is performed for validation of the thermodynamical stability of the molecule designed. This thesis is divided into three main sections, first a case study of the machine learning approach used in this work for properties predictions, follow of two case studies of molecular design coupled where the initial design is obtained by computer aided molecular design (CAMD) follows of the validation with molecular dynamics or machine learning techniques. In the first case a neural network analysis was performed to improve the predictions of 15 properties. Results of input analysis shown that reduced coulomb matrix saves CPU time and memory compared with full coulomb matrix with a similar accuracy. On the other hand, the neural network architecture exhibits the importance of the activation function, and the number of hidden layers in the neural network. Second case present the methodology to design a lubricant from scratch, using CAMD with group contribution methodologies to make a design of an Ionic Liquid-based lubricant with the purpose of use as automotive lubricant; then a molecular dynamics simulation was performed to validate the value of the design properties, finally a Monte Carlo simulation was performed to observe the thermodynamic feasibility of the molecule design. The result shows a feasible molecule previously designed where experimental values are similar with estimations of group contribution and molecular dynamics. In the third case, a classical CAMD methodology is used to make a list of molecule designs for photocatalytic applications, where the target property is the maximum absorption wave length (lmax); on the other hand, deep learning techniques is applied to predict the gap between HUMO and LUMO energies for the estimation of lmax showing a shorter list of candidates to experimental test, in the same way the NN used in this study can extend to other optical or photocatalytic applications, because HUMO and LUMO energies rules most of the photochemical properties. Finally, the mixture design is presented; however, the validations is left for future work.