Special issue: Mapping the Molecular Universe by Multiscale High-Throughput Screening and Machine Learning

Chemical space contains an enormous number of possible molecules, far too many to systematically enumerate. As this immense scale makes exhaustive experimental or computational enumeration effectively impossible, the key challenge in modern chemical space exploration is not to catalogue every molecule, but to intelligently navigate chemical space to identify molecules with useful properties or applications capable of addressing specific scientific or technological challenges.

Two years ago, scientific leaders in the field of chemical space exploration met and discussed the latest developments at the 2nd SIMPLAIX Workshop on Machine Learning for Multiscale Molecular Modeling (https://simplaix-workshop2024.h-its.org/) and the Chemical Compound Space Conference 2024 (CCSC2024, https://ccsc2024.github.io/ ). Following these two meetings, SIMPLAIX Principal Investigators Anya Gryn’ova, Rebecca Wade, Tristan Bereau, and Pascal Friederich teamed up with the editors of the Journal of Chemical Information and Modeling (published by the American Chemical Society), to solicit articles for a virtual special issue (VSI) on “Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning” (https://pubs.acs.org/doi/10.1021/acs.jcim.4c01300).

Two years later, the special issue has been published, containing 45 articles which highlight the cutting-edge research presented at these meetings and subsequent SIMPLAIX workshops (https://simplaix-workshop2025.h-its.org/; https://simplaix-workshop2026.h-its.org/) and disseminate the state-of-the-art achievements in chemical space exploration and chemoinformatics  (https://pubs.acs.org/jcisd8/article/66/12/6809/5162560/Mapping-the-Molecular-Universe-Exploring-Chemical).

Original research articles in the special issue co-authored by SIMPLAIX doctoral students and PIs:

Multi-Solvent Graph Neural Network for Reduction Potential Prediction Across the Chemical Space
Rostislav Fedorov; Anastasiia Nihei; Ganna Gryn’ova
Journal of Chemical Information and Modeling
January 12, 2026
https://doi.org/10.1021/acs.jcim.5c01450

Martignac: Computational Workflows for Reproducible, Traceable, and Composable Coarse-Grained Martini Simulations
Tristan Bereau; Luis J. Walter; Joseph F. Rudzinski
Journal of Chemical Information and Modeling
December 02, 2024

The Black Hole Strategy: Gravity-Based Representative Sampling for Frugal Graph Learning on Metal–Organic Framework Networks
Mehrdad Jalali; A. D. Dinga Wonanke; Pascal Friederich; Christof Wöll
Journal of Chemical Information and Modeling
October 01, 2025
https://doi.org/10.1021/acs.jcim.5c01518