ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning

Digital Discovery, Journal Year: 2024, Volume and Issue: 3(7), P. 1389 - 1409

Published: Jan. 1, 2024

ProtAgents is a de novo protein design platform based on multimodal LLMs, where distinct AI agents with expertise in knowledge retrieval, structure analysis, physics-based simulations, and results analysis tackle tasks dynamic setting.

Language: Английский

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Biophysics-based protein language models for protein engineering

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 17, 2024

Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure, and function. However, these overlook decades of research into biophysical factors governing We propose Mutational Effect Transfer Learning (METL), a model framework that unites advanced machine learning modeling. Using the METL framework, we pretrain transformer-based neural networks simulation to capture fundamental relationships between energetics. finetune experimental sequence-function harness signals apply them when predicting properties like thermostability, catalytic activity, fluorescence. excels in challenging engineering tasks generalizing from small training sets position extrapolation, although existing methods train remain many types assays. demonstrate METL's ability design functional green fluorescent variants only 64 examples, showcasing potential biophysics-based engineering.

Language: Английский

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AlloyGPT: End-to-end prediction and design of additively manufacturable alloys using an autoregressive language model

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 7, 2025

Rapid progress in additive manufacturing of alloys opens opportunities controlling compositions and microstructures at voxel-size resolution complex geometries, thus unlocking unprecedented design performance various critical engineering applications. However, to fully exploit such potential, capable yet efficient models for navigating the vast spaces alloy compositions, structures properties are great research interest. Here, we present AlloyGPT, an autoregressive alloy-specific language model, that learns composition-structure-property relationship generates novel designs additively manufacturable alloys. Specifically, develop grammar convert physics-rich datasets into readable text records both forward prediction inverse tasks. Then, construct a customized tokenizer generative pre-trained transformer (GPT) model master this through training. At deployment, our can accurately predict multiple phase based on given achieving R2 values ranging from 0.86 0.99 test set. When tested beyond learned composition domain, only degrades gradually stable manner. Given desired structures, same suggest meet goals. And balance between diversity accuracy be further tuned stably. Our AlloyGPT presents way integrating comprehensive knowledge terms simultaneously solve tasks with accuracy, robustness. This fundamental will open new avenues accelerate integration material pure or gradient structural manufactured by traditional manufacturing.

Language: Английский

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Generative artificial intelligence for enzyme design: Recent advances in models and applications

Current Opinion in Green and Sustainable Chemistry, Journal Year: 2025, Volume and Issue: 52, P. 101010 - 101010

Published: March 2, 2025

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Automating alloy design and discovery with physics-aware multimodal multiagent AI

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(4)

Published: Jan. 24, 2025

The design of new alloys is a multiscale problem that requires holistic approach involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, process typically slow reserved for human experts. Machine learning can help accelerate this process, instance, through use deep surrogate models connect structural chemical features to material properties, or vice versa. However, existing data-driven often target specific objectives, offering limited flexibility integrate out-of-domain knowledge cannot adapt new, unforeseen challenges. Here, we overcome these limitations by leveraging distinct capabilities multiple AI agents collaborate autonomously within dynamic environment solve complex materials tasks. proposed physics-aware generative platform, AtomAgents, synergizes intelligence large language (LLMs) collaboration among with expertise in various domains, including retrieval, multimodal data integration, physics-based simulations, comprehensive results analysis across modalities. concerted effort multiagent system allows addressing problems, as demonstrated examples include designing metallic enhanced properties compared their pure counterparts. Our enable accurate prediction key characteristics highlight crucial role solid solution alloying steer development alloys. framework enhances efficiency multiobjective tasks opens avenues fields such biomedical engineering, renewable energy, environmental sustainability.

Language: Английский

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Capturing the Impact of Protein Unfolding on the Dynamic Assembly of Protein Networks

Soft Matter, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Protein force liability leads to dynamic change in building block shape, i.e. unfolding or changes folded resulting a three-phase assembly process.

Language: Английский

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Evolving Biomaterials Design from Trial and Error to Intelligent Innovation

Acta Biomaterialia, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Autonomous Bioelectronic Devices Based on Silk Fibroin

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: March 23, 2025

Abstract The development of autonomous bioelectronic devices capable dynamically adapting to changing biological environments represents a significant advancement in healthcare and wearable technologies. Such systems draw inspiration from the precision, adaptability, self‐regulation processes, requiring materials with intrinsic versatility seamless bio‐integration ensure biocompatibility functionality over time. Silk fibroin (SF) derived Bombyx mori cocoons, has emerged as an ideal biomaterial unique combination biocompatibility, mechanical flexibility, tunable biodegradability. Adding features into SF, including self‐healing, shape‐morphing, controllable degradation, enables dynamic interactions living tissues while minimizing immune responses mismatches. Additionally, structural tunability environmental sustainability SF further reinforce its potential platform for adaptive implants, epidermal electronics, intelligent textiles. This review explores recent progress understanding structure–property relationships modification strategies, great integration advanced addressing challenges related scalability, reproducibility, multifunctionality. Future opportunities, such AI‐assisted material design, scalable fabrication techniques, incorporation wireless personalized technologies, are also discussed, positioning key bridging gap between artificial

Language: Английский

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Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomolecules

Journal of Molecular Biology, Journal Year: 2024, Volume and Issue: unknown, P. 168818 - 168818

Published: Oct. 1, 2024

Language: Английский

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A survey of emerging applications of large language models for problems in mechanics, product design, and manufacturing

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 64, P. 103066 - 103066

Published: Dec. 27, 2024

Language: Английский

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