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The future of practice: Enabling teachers to create learning interactives with generative UI

Google Research Blog · research.google · September 17, 2026

Education Innovation

Read on Google Research Blog → Opens the original article in a new tab.

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Microsoft Research Blog Nov 3, 2026

Learn the rules: The hacker mindset

Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.

Google DeepMind Blog Nov 3, 2026

Agentic cheminformatics with Google Antigravity

Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.

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#machine learning Open access May 2025

Token-Mol 1.0: tokenized drug design with large language models

Token-Mol is presented, a token-only 3D drug design model that encodes both 2D and 3D structural information, along with molecular properties, into discrete tokens, which introduces a Gaussian cross-entropy loss function tailored for regression tasks, enabling superior performance across multiple downstream application...

Ji-Ke Wang, Rui Qin, Mingyang Wang et al. · 30 citations · ⚡1
#machine learning Open access Jul 2025

RSGPT: a generative transformer model for retrosynthesis planning pre-trained on ten billion datapoints

RSGPT, a generative model pre-trained on ten billion data points, achieving state-of-the-art performance for synthesis planning, and introduces reinforcement learning to capture the relationships among products, reactants, and templates more accurately.

Yafeng Deng, Xinda Zhao, Hanyu Sun et al. · 18 citations · ⚡2

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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