Add support for Adaptyv for protein design

docs/scientific-skills.md

@@ -96,6 +96,7 @@
 - **PyHealth** - Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. Provides specialized tools for electronic health records (EHR), physiological signals, medical imaging, and clinical text analysis. Key features include: 10+ healthcare datasets (MIMIC-III/IV, eICU, OMOP, sleep EEG, COVID-19 CXR), 20+ predefined clinical prediction tasks (mortality, hospital readmission, length of stay, drug recommendation, sleep staging, EEG analysis), 33+ models (Logistic Regression, MLP, CNN, RNN, Transformer, GNN, plus healthcare-specific models like RETAIN, SafeDrug, GAMENet, StageNet), comprehensive data processing (sequence processors, signal processors, medical code translation between ICD-9/10, NDC, RxNorm, ATC systems), training/evaluation utilities (Trainer class, fairness metrics, calibration, uncertainty quantification), and interpretability tools (attention visualization, SHAP, ChEFER). 3x faster than pandas for healthcare data processing. Use cases: ICU mortality prediction, hospital readmission risk assessment, safe medication recommendation with drug-drug interaction constraints, sleep disorder diagnosis from EEG signals, medical code standardization and translation, clinical text to ICD coding, length of stay estimation, and any clinical ML application requiring interpretability, fairness assessment, and calibrated predictions for healthcare deployment
 
 ### Protein Engineering & Design
+- **Adaptyv** - Cloud laboratory platform for automated protein testing and validation. Submit protein sequences via API or web interface and receive experimental results in approximately 21 days. Supports multiple assay types including binding assays (biolayer interferometry for protein-target interactions, KD/kon/koff measurements), expression testing (quantify protein expression levels in E. coli, mammalian, yeast, or insect cells), thermostability measurements (DSF and CD for Tm determination and thermal stability profiling), and enzyme activity assays (kinetic parameters, substrate specificity, inhibitor testing). Includes computational optimization tools for pre-screening sequences: NetSolP/SoluProt for solubility prediction, SolubleMPNN for sequence redesign to improve expression, ESM for sequence likelihood scoring, ipTM (AlphaFold-Multimer) for interface stability assessment, and pSAE for aggregation risk quantification. Platform features automated workflows from expression through purification to assay execution with quality control, webhook notifications for experiment completion, batch submission support for high-throughput screening, and comprehensive results with kinetic parameters, confidence metrics, and raw data access. Use cases: antibody affinity maturation, therapeutic protein developability assessment, enzyme engineering and optimization, protein stability improvement, AI-driven protein design validation, library screening for expression and function, lead optimization with experimental feedback, and integration of computational design with wet-lab validation in iterative design-build-test-learn cycles
 - **ESM (Evolutionary Scale Modeling)** - State-of-the-art protein language models from EvolutionaryScale for protein design, structure prediction, and representation learning. Includes ESM3 (1.4B-98B parameter multimodal generative models for simultaneous reasoning across sequence, structure, and function with chain-of-thought generation, inverse folding, and function-conditioned design) and ESM C (300M-6B parameter efficient embedding models 3x faster than ESM2 for similarity analysis, classification, and feature extraction). Supports local inference with open weights and cloud-based Forge API for scalable batch processing. Use cases: novel protein design, structure prediction from sequence, sequence design from structure, protein embeddings, function annotation, variant generation, and directed evolution workflows
 
 ### Machine Learning & Deep Learning