Looking beyond natural sequences
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
More from the blog
Researchers improve weather forecasts looking two to six weeks out
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
GPT-Lab
· gpt-lab.eu
Oct 14, 2026
Building trustworthy AI: The role of AI harnesses, intent contracts and evals
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post Building trustworthy AI: The role of AI harnesses, intent contracts and evals appeared first on GPT-Lab.
GPT-Lab
· gpt-lab.eu
Oct 12, 2026
AI Has Grown Up. Have We?
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.
GPT-Lab
· gpt-lab.eu
Oct 1, 2026
Zotero Tutorialero
Zotero is a powerful open source reference manager that looks complicated at first, but is worth configuring properly once with plugins. This tutorial is aimed especially at researchers and doctoral students who want to use Zotero to manage literature and want a more efficient workflow. The post Zotero Tutorialero appeared first on GPT-Lab.
Related papers
What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
PepPCBench is a Comprehensive Benchmarking Framework for Protein-Peptide Complex Structure Prediction
PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.
PepBAN: A Deep Learning Framework with Bilinear Attention and Adversarial Learning for Peptide-Protein Interaction Prediction
PepBAN is introduced, a deep learning framework for modeling PepPI predictions that effectively learns the pattern of pairwise local interactions, enables the identification of key residues participating in the peptide-protein interactions, and offers an intuitive approach to interpret the underlying mechanisms of PepP...
Computational and AI-Driven Ecosystem for Structure-Based Covalent Drug Discovery.
This Account describes a computational and AI-driven ecosystem for structure-based covalent drug discovery and dives into a suite of cutting-edge, AI-driven computational methods, exploring the potential of deep learning in tasks such as molecular docking, covalent binding site prediction, and lead optimization.