Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Cellular Automata and Applications
Abstract
This paper introduces a novel adaptive multi-dynamics simulation framework designed to model complex biological systems and optimize biological engineering design through automated parameter adjustment based on simulation results. Traditional multi-dynamics simulations often require extensive manual parameter tuning, hindering efficiency and potentially limiting the model's ability to capture emergent behavior. Our approach leverages self-adaptive algorithms to dynamically adjust simulation parameters, leading to significantly improved simulation speed and the potential for more robust and realistic biological system modeling. We demonstrate the framework's effectiveness through illustrative examples of simulating cellular automata and protein folding, highlighting its ability to generate complex, dynamic behaviors. The core mechanism centers around a feedback loop that iteratively refines simulation parameters to achieve desired outcomes.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
