Machine Learning Methods for Bioinformatics

Dear Colleagues,

Machine learning is crucial for modern molecular informatics, driving unprecedented progress in predicting, designing, and optimizing small molecules and proteins. However, as computational models grow in sophistication—from high-fidelity binding affinity predictors to generative architectures exploring vast chemical spaces—key challenges persist. Sparse and noisy biological datasets, the combinatorial complexity of molecular interactions, and the need for interpretable frameworks necessitate the development of algorithms that balance innovation with biophysical fidelity.

This Special Issue calls for methodological advances in machine learning tailored to bioinformatics, with a focus on computationally rigorous solutions in the following areas: prediction (e.g., geometric deep learning for 3D protein–ligand dynamics), design (e.g., constrained diffusion models for synthesizable compounds), and screening (e.g., federated learning across distributed chemical libraries). We seek approaches addressing problems such as targeting cryptic binding pockets, navigating multi-objective ADMET landscapes, or engineering functional protein conformations, particularly those integrating domain knowledge through physics-based priors, evolutionary constraints, or pharmacophoric rules. We additionally welcome high-quality papers on ML pertaining to genomics, proteomics, and biomedical imaging.

Submissions must demonstrate methodological novelty with in silico validation across various benchmarks—including robustness tests on out-of-distribution targets, scalability in ultra-large virtual screens, or consistency with established structural/energetic principles. Open-source implementations and reproducible workflows are particularly sought, aiming to accelerating community adoption. By curating algorithms that translate theoretical advances into actionable computational tools, this Special Issue aims to redefine the standards of ML-driven molecular exploration.

Dr. Xianggen Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• geometric deep learning
• de novo molecular design
• protein–ligand interaction
• physics-informed generative models
• virtual screening
• multi-objective ADMET optimization
• protein–protein interaction