ArticleThe journal of physical chemistry. B2025
Toward the Evolutionary Optimisation of Small Molecules Within Coarse-Grained Simulations: Training Molecules to Hide Behind Lipid Head Groups.
Article in The journal of physical chemistry. B, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Automated parametrization of small molecules within the Martini 3 coarse-grained model guided by experimental log P values.Scientific reports · 2025Article
- Navigating chemical space: multi-level Bayesian optimization with hierarchical coarse-graining.Chemical science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Exploring the vast chemical space of small molecules poses a significant challenge. We develop a new strategy to efficiently explore this space using coarse-grained toy-like molecules utilizing the Martini3 force field and graph representations. This yields initial proof-of-concept results for the approach enabling the identification of optimal molecules with specific properties targeting lipid bilayers. By leveraging genetic algorithms and coarse-grained molecular dynamics simulations, we demonstrate the potential of our method in designing simple, linear molecules. Our findings show a good convergence toward molecules with weak amphiphilic properties, resembling known (general anesthetic) molecules. While this study demonstrates the feasibility of our method, further refinement is needed to fully realize its potential and explore more complex molecular topologies. Nevertheless, these encouraging results suggest a new path for future research in small molecule discovery and design without relying on extensive data sets.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.