arXiv:2512.00283v3 Announce Type: replace Abstract: Foundation models have revolutionized various fields such as natural language processing (NLP) and computer vision (CV). While efforts have been made to transfer the success of the foundation models in general AI domains to biology, existing works focus on directly adopting the existing foundation model architectures from general machine learning domains without a systematic design considering the unique physicochemical and structural properties of each biological data modality. This leads to suboptimal performance, as these repurposed archit
The rapid advancement of foundation models in general AI domains is naturally leading to efforts to adapt them for specialized scientific fields like biology, driven by the increasing availability of biological data.
Biologically-aware AI architectures can significantly accelerate drug discovery, materials science, and fundamental biological research by more effectively modeling complex molecular and cellular systems.
The focus is shifting from direct adoption of general AI architectures to developing specialized neural architectures that explicitly consider unique biological properties, promising more optimal and impactful biological foundation models.
- · Biotech companies
- · Pharmaceutical research
- · AI algorithm developers
- · Synthetic biology
- · Generic AI model developers (without biological specialization)
- · Traditional drug discovery methods
Improved performance of AI models in biological applications due to tailored architectures.
Faster development cycles for new therapeutics, biomaterials, and agricultural solutions.
The emergence of entirely new industries and treatment modalities based on AI-designed biological systems.
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Read at arXiv cs.LG