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Looking beyond natural sequences

MIT News · Artificial Intelligence · news.mit.edu · By Lillian Eden | Department of Biology · August 27, 2026

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.

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Microsoft Research Blog Oct 22, 2026

Flint-interactive: A language for creating interactive data visualizations

Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.

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#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

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PepPCBench is a Comprehensive Benchmarking Framework for Protein-Peptide Complex Structure Prediction

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Si-Long Zhai, Huifeng Zhao, Ji-Ke Wang et al. · 13 citations · ⚡1

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...

Shuaiyan Li, Xiaorui Wang, Yuchen Zhu et al. · 4 citations

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.

Shi Li, Hongyan Du, Xujun Zhang et al. · 4 citations

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