Markov chain Monte Carlo

Janus

Algorithm

Demography

Deep neural networks

Bayesian probability

Materials science

Theoretical computer science

Inverse

Computer science

Mathematical optimization

Filter (signal processing)

Environmental health

Parallel tempering

Monte Carlo molecular modeling

Mathematics

Genetic algorithm

Sampling (signal processing)

Population

Sociology

Artificial intelligence

Geometry

Composite material

Tempering

Medicine

Programming language

Deep learning

Artificial neural network

Machine learning

Computer vision

Digital Discovery

Inverse molecular design involves algorithms that sample molecules with specific target properties from a multitude of candidates and can be posed as an optimization problem. High-dimensional optimization tasks in the natural sciences are commonly tackled via population-based metaheuristic optimization algorithms such as evolutionary algorithms. However, often unavoidable expensive property evaluation can limit the widespread use of such approaches as the associated cost can become prohibitive. Herein, we present JANUS, a genetic algorithm inspired by parallel tempering. It propagates two populations, one for exploration and another for exploitation, improving optimization by reducing property evaluations. JANUS is augmented by a deep neural network that approximates molecular properties and relies on active learning for enhanced molecular sampling. It uses the SELFIES representation and the STONED algorithm for the efficient generation of structures, and outperforms other generative models in common inverse molecular design tasks achieving state-of-the-art target metrics across multiple benchmarks. As neither most of the benchmarks nor the structure generator in JANUS account for synthesizability, a significant fraction of the proposed molecules is synthetically infeasible demonstrating that this aspect needs to be considered when evaluating the performance of molecular generative models.

Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design