Revolutionizing Antibody Ranking with Contextual Learning
AbICL is reshaping antibody discovery by leveraging In-Context Learning to enhance affinity ranking. Its success on benchmarks underscores a shift towards context-aware models in therapeutics.
Read the full articleYou might also wanna read
Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions
Analysis of multimodal and multidimensional data capturing dynamic interactions between diverse cell populations is a current challenge in b
Gemma-Based AI Model Identifies Potential Cancer Therapy Pathway Through Conditional Immune Amplification
We’re launching a new 27 billion parameter foundation model for single-cell analysis built on the Gemma family of open models.
Global protein-ligand binding affinity profiling via photocatalytic labeling
New approach integrates protein-ligand binding affinity profiling and photocatalytic labeling into a single step, enhancing drug discovery w
AI-Driven Protein Binder Design: Progress and Pitfalls in Evaluation Practices
Build better molecules with frontier AI models. With AI, we help every scientist reshape biology.

AI-guided discovery identifies GPNMB as promising CAR T cell target for multiple cancers
An AI-guided framework integrating high-resolution cellular atlases, public knowledge databases, and large language models enables a modular
Benchmarking Codon Optimization Strategies for Improved Antibody Expression
Webinar Codon Optimization Isn’t Equal: Benchmarking Gene Design for Antibody Expression On-demand Webinar Watch the Webinar Key takeaways i

Comments
Sign in to join the conversation.
No comments yet. Be the first.