Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

Abstract

Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.

EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation

“…The objective of our work is to design a molecular generator capable of exploring known as well as unfamiliar areas of the chemical space. Between drug-like generation and organic materials, the chemical space of interest is different [ 30 ]. The molecular materials space is less known and has probably been only intensively searched around the few known successes.…”

A Refractive Index Study of a Diverse Set of Polymeric Materials by QSPR with Quantum-Chemical and Additive Descriptors

“…Differing techniques have been used to model the refractive index of differing materials such as using the Lorentz–Lorenz equation, Mie Theory, the Rayleigh–Debye–Gans theory, the implementation of group contribution method, and the use of quantitative structure property relationship (QSPR) methodology [ 22 , 23 ]. QSPR has been used as a powerful tool to predict the properties of various chemical systems and materials for the last three decades [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. It is worth noting that an interesting approach to predict the refractive index for aerosols can be applicable for polymers, which is represented in the work [ 29 ].…”

MolFinder: An Evolutionary Algorithm for the Global Optimization of Molecular Properties and the Extensive Exploration of Chemical Space Using SMILES

“…Together with the VAE approach, RL-VAE models that improve the fitness of molecules produced by VAE via RL have been suggested [17,18]. More comprehensive reviews of various ML-based molecular generation and optimization methods are given in detail in recent papers [4,19,20,21].…”