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Cast your votes for the most impactful computational biology and BioAI platforms of 2026. Votes are saved to the cloud database.
آرای ثبتشده در این نظرسنجی در دیتابیس ابری ثبت میشوند و نتایج نهایی به صورت زنده تجمیع و نمایش داده میشوند.
Honoring the computational models, open-source algorithms, and lab automation platforms that are redefining the boundaries of medicine and biological engineering.
Recognizing the model that has most profoundly advanced zero-shot prediction and macromolecular generation across sequence, structure, and dynamics.
Diffusion-based all-atom prediction for protein-DNA-RNA-ligand complexes.
Simultaneous multimodal generation across sequence, structure, and functional active sites.
Multimodal biomolecular structure prediction with open weights and commercial friendliness.
7-billion parameter long-context genomic transformer predicting whole operons.
The highest-performing machine learning architectures for molecular docking, ultra-large library screening, and ADMET profiling.
Equivariant diffusion over rigid body and torsional degrees of freedom.
Open-source all-atom biomolecular model rivaling commercial proprietary systems.
3D molecular pre-training architecture for quantum chemistry and binding affinity.
Championing transparent, reproducible, and community-owned software in life sciences.
Generative protein backbone scaffolding by reversing the noise process.
Fast and robust deep learning sequence design for fixed protein backbones.
Trainable, PyTorch-native recreation of AlphaFold2 with complete training code.
Transforming wet-lab execution through AI-guided robotic orchestration and cloud laboratories.
Liquid handling with onboard computer vision, LLM protocol generation, and API control.
Remote-controlled automated laboratory executing hundreds of standard analytical assays.
Modular robotic arms connecting multi-vendor diagnostic and screening instruments.