Boston | AI-Driven Computational Modeling for Structural Biology and Protein Engineering

Organizer	Submission Deadline	Notification of Acceptance	Submission Email	Download
College of Engineering, Northeastern University	June 18, 2027	7-20 workdays	sympo_boston@confmpcs.org	Manuscript Template

About

Background

Artificial intelligence and deep learning are rapidly transforming biomolecular engineering by enabling increasingly accurate modeling of protein structure, molecular interactions, and sequence–structure–function relationships. Advances in protein language models, structure prediction, and geometric deep learning have created new opportunities to move beyond structural characterization toward the rational design and engineering of biomolecular systems. These computational approaches can accelerate the identification of functional residues, prediction of protein–protein interactions, assessment of molecular stability, and prioritization of candidate designs before experimental validation. Integrating AI with structural and molecular information therefore offers a promising framework for addressing challenging problems in protein engineering, enzyme engineering, therapeutic development, and other areas of biomolecular engineering. The symposium, which serves as a specialized session of the 5th International Conference on Mathematical Physics and Computational Simulation (CONF-MPCS 2027), will focus on artificial intelligence and modeling.

Goal/Rationale

Despite rapid advances in AI-based biomolecular modeling, translating predictive performance into practical engineering applications remains challenging. Biomolecular systems are structurally complex, experimental datasets are often limited, and models must generalize to previously unseen proteins, conformations, and molecular interactions. Furthermore, understanding why an AI model makes a particular prediction is increasingly important when computational results are used to guide experimental design. This symposium aims to bring together researchers working at the intersection of artificial intelligence, structural biology, and biomolecular engineering to discuss methods that bridge computational prediction and engineering applications. Particular emphasis will be placed on structure-aware and interpretable machine-learning approaches, protein and enzyme engineering, molecular interaction modeling, and the integration of computational predictions with experimental workflows. The symposium will encourage discussion of both methodological advances and their translation into practical biomolecular design and engineering problems.

Scope

The symposium welcomes researchers and students working on computational and experimental approaches to biomolecular and protein engineering. Relevant topics include AI and machine learning for protein structure and function; protein–protein and antibody–antigen interactions; protein and enzyme engineering; structure-based prediction and design; geometric and graph neural networks for biomolecular systems; protein language models and multimodal biological models; molecular stability and dynamics; interpretable AI for biomolecular applications; computational screening and candidate prioritization; and integration of AI predictions with experimental engineering workflows. Contributions involving methodological developments, benchmark studies, experimental validation, and applications to therapeutics, enzymes, antibodies, or other engineered biomolecular systems are encouraged.

Publication

Accepted papers of the symposium will be published in Theoretical and Natural Science (TNS) (Print ISSN 2753-8818), and will be submitted to Conference Proceedings Citation Index (CPCI), Crossref, CNKI, Portico, Google Scholar, and other databases for indexing. The situation may be affected by factors among databases like processing time, workflow, policy, etc.

Publication info

Title: Theoretical and Natural Science (TNS)
Press: EWA Publishing, United Kingdom
ISSN: 2753-8818, 2753-8826 (electronic)

This symposium is organized by CONF-MPCS 2027 and it will independently proceed the submission and publication process.

* The papers will be exported to production and publication on a regular basis. Early-registered papers are expected to be published online earlier.