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Tuesday

  1. Google DeepMindSelected

    Google DeepMind launches Cheminformatics Skills for Antigravity

    Google DeepMind has released a set of Cheminformatics Skills for Google Antigravity. Built on RDKit and connected to ChEMBL and PubChem, the language model handles task orchestration and scientific reasoning, while deterministic chemistry tools perform molecular computations. Using lung cancer drugs as an example, the agent searched thousands of compounds in about one hour, completed matched molecular pair analysis, trained a potency prediction model, and also proposed novel rigidified macrocyclic molecular designs with synthetic routes. The team also noted that sparse data, inter-laboratory assay noise, and narrow dynamic ranges limit prediction accuracy; computational designs are only verifiable hypotheses and still require experimental synthesis for validation. The skills are now live on Antigravity and can be used by installing the Science plugin, and free access to the highest usage tier for six months is being offered to 10,000 scientists.

    An image showing a text input box in a clean, light-themed user interface. The input box contains a prompt asking to search academic and patent literature for macrocyclic and acyclic compounds in the crizotinib and lorlatinib series. A dropdown menu at the bottom-left shows "Gemini 1.5 Flash" selected.

    Google DeepMind has released a set of Cheminformatics Skills for Google Antigravity. Built on RDKit and connected to ChEMBL and PubChem, the language model handles task orchestration and scientific reasoning, while deterministic chemistry tools perform molecular computations. Using lung cancer drugs as an example, the agent searched thousands of compounds in about one hour, completed matched molecular pair analysis, trained a potency prediction model, and also proposed novel rigidified macrocycl

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    #lab#Product updates#Agents#English text is being prepared.
  2. FactRelated story

    Google DeepMind launches cheminformatics skills for Antigravity

    On November 3, 2026, Google DeepMind released a cheminformatics skills set (Cheminformatics Skills) for Google Antigravity. The skill is based on RDKit, connects to ChEMBL and PubChem, and has a language model responsible for task orchestration and scientific reasoning, while deterministic chemistry tools perform molecular computations. Taking a lung cancer drug as an example, the agent retrieved thousands of compounds within about one hour, completed matched molecular pair analysis and trained a potency prediction model, and also proposed novel rigidified macrocyclic molecule designs with synthetic routes. Google DeepMind also noted that sparse data, inter-laboratory assay noise and narrow dynamic range can limit prediction accuracy, and that computational design is only a verifiable hypothesis that still requires experimental synthesis for validation. The skill is now live on Antigravity and can be used by installing the Science plugin, and free access to the highest usage tier for six months is being offered to 10,000 scientists.

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    View story:Google DeepMind launches cheminformatics skill for Antigravity

Saturday

  1. Google DeepMindSelected

    Google DeepMind's PlanAssist to expand to 12 UK local planning authorities

    PlanAssist, an AI planning tool developed by Google DeepMind in collaboration with the UK government, will expand to another 12 local planning authorities in England, bringing the total number of participants to 15. In early pilots in Barnet, Camden and Dorset, the tool reduced the average time planning officers spent on assessment tasks for household residential applications by 58%, and more than 70% of generated reports were approved by officers without modification. Built on Gemini, PlanAssist verifies application materials and drawings, checks against heritage protection restrictions and local and national planning policy, reviews prior objections, and drafts reports with citations, while the final decision remains with planning officers. The tool has been in pilot since June and is now entering a 12-month Beta phase; if testing goes smoothly, the UK government plans to make it available to all local councils in England in 2027.

    Abstract 3D illustration of miniature houses on layered, wavy terrain with architectural plans in the foreground.

    PlanAssist, an AI planning tool developed by Google DeepMind in collaboration with the UK government, will expand to another 12 local planning authorities in England, bringing the total number of participants to 15. In early pilots in Barnet, Camden and Dorset, the tool reduced the average time planning officers spent on assessment tasks for household residential applications by 58%, and more than 70% of generated reports were approved by officers without modification. Built on Gemini, PlanAssis

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    #lab#Industry news#Agents#Google

Thursday

  1. Google DeepMind

    Google DeepMind, together with WRI and IIASA, releases ForTy 2020, a 10-meter-resolution global forest type map

    Google DeepMind and Google Research, together with the World Resources Institute (WRI) and the International Institute for Applied Systems Analysis (IIASA), released Forest Typology (ForTy) 2020, which classifies every 10×10-meter parcel of land worldwide into six forest and land-use types, serving as a key baseline for the European Union's deforestation regulation. The team trained the model on about 1.7 million labeled locations and used AlphaEarth Foundations embedding vectors as a key input; in validation at more than 9,000 randomly sampled points, the forest type classification accuracy was 90%, and the accuracy in distinguishing forest from non-forest was 95%. A companion year-by-year time series from 2017 to 2025 is also openly available and can be used free of charge under an open license on Google Earth Engine.

    A close-up of a 3D voxel-style world map focusing on the blocky terrain of South America.

    Google DeepMind and Google Research, together with the World Resources Institute (WRI) and the International Institute for Applied Systems Analysis (IIASA), released Forest Typology (ForTy) 2020, which classifies every 10×10-meter parcel of land worldwide into six forest and land-use types, serving as a key baseline for the European Union's deforestation regulation. The team trained the model on about 1.7 million labeled locations and used AlphaEarth Foundations embedding vectors as a key input;

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    #lab#Papers/Research#English text is being prepared.#Google

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Wednesday

  1. Story1 reports · 1 facts

    Google DeepMind launches cheminformatics skill for Antigravity

    On November 3, 2026, Google DeepMind released a cheminformatics skills set (Cheminformatics Skills) for Google Antigravity. The skill is based on RDKit, connects to ChEMBL and PubChem, and has a language model responsible for task orchestration and scientific reasoning, while deterministic chemistry tools perform molecular computations. Taking a lung cancer drug as an example, the agent retrieved thousands of compounds within about one hour, completed matched molecular pair analysis and trained a potency prediction model, and also proposed novel rigidified macrocyclic molecule designs with synthetic routes. Google DeepMind also noted that sparse data, inter-laboratory assay noise and narrow dynamic range can limit prediction accuracy, and that computational design is only a verifiable hypothesis that still requires experimental synthesis for validation. The skill is now live on Antigravity and can be used by installing the Science plugin, and free access to the highest usage tier for six months is being offered to 10,000 scientists.

    On November 3, 2026, Google DeepMind released a cheminformatics skills set (Cheminformatics Skills) for Google Antigravity. The skill is based on RDKit, connects to ChEMBL and PubChem, and has a language model responsible for task orchestration and scientific reasoning, while deterministic chemistry tools perform molecular computations. Taking a lung cancer drug as an example, the agent retrieved thousands of compounds within about one hour, completed matched molecular pair analysis and trained

    Matched fieldsTitle