Application of Artificial Intelligence in Chemistry

All the problems can be solved with the help of machines mainly computers using algorithm and by interpreting their output data is considered as artificial intelligence (AI). Artificial intelligence is faster than manual work, reduces manpower, more efficient and accurate and used in various field these days and coming up with more advanced technology. With the help of artificial intelligence, drugs can be formulated and produced in an advanced way. New machineries’ used in chemical or pharmaceutical labs are much advanced these days, that reduces the time of the analysis. There is a strong bond between artificial intelligence and chemistry. In the field of chemistry designing new molecules, molecular property detection of molecules and compounds, drug discovery, synthesis and retrosynthesis of molecules, analysis prediction for better and accurate results, all these can be done with the help of artificial intelligence.

Quantum Mechanics Enables "Freedom of Design" in Molecular Property Space

Rational design of molecules with targeted properties requires understanding quantum-mechanical (QM) structure-property/property-property relationships (SPR/PPR) across chemical compound space. We analyze these relationships using the QM7-X dataset---which includes multiple QM properties for ~4.2 M equilibrium and non-equilibrium structures of small (primarily organic) molecules. Instead of providing simple SPR/PPR that strictly follow physicochemical intuition, our analysis uncovers substantial flexibility in molecular property space (MPS) when searching for a single molecule with a desired pair of QM properties or distinct molecules with a targeted set of QM properties. As proof-of-concept, we used Pareto multi-property optimization to search for the most promising (i.e., highly polarizable and electrically stable) molecules for polymeric battery materials; without prior knowledge of this complex manifold of MPS, Pareto front analysis reflected this intrinsic flexibility and identified small directed structural/compositional changes that simultaneously optimize these properties. Our analysis of such extensive QM property data provides compelling evidence for an intrinsic “freedom of design” in MPS, and indicates that rational design of molecules with a diverse array of targeted QM properties is quite feasible.

A general optimization protocol for molecular property prediction using a deep learning network

The key to generating the best deep learning model for predicting molecular property is to test and apply various optimization methods. While individual optimization methods from different past works outside the pharmaceutical domain each succeeded in improving the model performance, better improvement may be achieved when specific combinations of these methods and practices are applied. In this work, three high-performance optimization methods in the literature that have been shown to dramatically improve model performance from other fields are used and discussed, eventually resulting in a general procedure for generating optimized CNN models on different properties of molecules. The three techniques are the dynamic batch size strategy for different enumeration ratios of the SMILES representation of compounds, Bayesian optimization for selecting the hyperparameters of a model and feature learning using chemical features obtained by a feedforward neural network, which are concatenated with the learned molecular feature vector. A total of seven different molecular properties (water solubility, lipophilicity, hydration energy, electronic properties, blood–brain barrier permeability and inhibition) are used. We demonstrate how each of the three techniques can affect the model and how the best model can generally benefit from using Bayesian optimization combined with dynamic batch size tuning.