# JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design Source: [JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:28:26.804Z Authors: AkshatKumar Nigam, Robert Pollice, Alan Aspuru-Guzik Published: 2021-06-07T23:41:34.000Z ## Abstract Inverse molecular design, i.e., designing molecules with specific target properties, 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, expensive property evaluation, which is often required, can limit the widespread use of such approaches as the associated cost can become prohibitive. Herein, we present JANUS, a genetic algorithm that is inspired by parallel tempering. It propagates two populations, one for exploration and another for exploitation, improving optimization by reducing expensive property evaluations. Additionally, JANUS is augmented by a deep neural network that approximates molecular properties via active learning for enhanced sampling of the chemical space. Our method uses the SELFIES molecular 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 performance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multi-objective optimization on OEM (organic electrode material) design case study](): Multi-Objective Optimization; OEM (organic electrode material) design case study; Final Hypervolume: 17.71. - [Multi-objective Optimization on ZINC-250k (logP-TPSA)](): Multi-Objective Optimization; ZINC-250k (logP-TPSA); Hypervolume: 2302.93. - [logP Optimization on ZINC-250k](): Molecular Property Optimization; ZINC 250K; logP: 16.14. ## Original sources and implementation [Original paper]() [PDF]()