{"id":436,"date":"2026-05-29T16:17:00","date_gmt":"2026-05-29T16:17:00","guid":{"rendered":"https:\/\/www.boltzmannmaps.com\/blog\/?p=436"},"modified":"2026-05-29T16:18:27","modified_gmt":"2026-05-29T16:18:27","slug":"admet-prediction-properties-to-enhance-computer-aided-drug-discovery","status":"publish","type":"post","link":"https:\/\/www.boltzmannmaps.com\/blog\/admet-prediction-properties-to-enhance-computer-aided-drug-discovery\/","title":{"rendered":"ADMET Prediction Properties to Enhance Computer-Aided Drug Discovery"},"content":{"rendered":"\n

Intelligent ADMET for Intelligent Design<\/em><\/em><\/h2>\n\n\n\n

Drug discovery has entered a new era, defined not only by how rapidly we can generate new molecules, but by how intelligently we can evaluate them. With our latest update to Boltzmann Maps (BMaps), we\u2019re bringing that intelligence one step further by incorporating MapLight Therapeutics\u2019 ADMET property prediction tools directly into BMaps.<\/p>\n\n\n\n

This integration enables researchers to analyze molecular designs made in BMaps with a holistic view of their pharmacokinetic potential. The improved potentials makes it easier than ever to identify compounds that are not only potent, but also likely to behave as real drugs in biological systems.<\/p>\n\n\n\n

MapLight ADMET<\/h2>\n\n\n\n

MapLight represents one of the more accurate and rapid AI-driven frameworks for predicting ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) properties across diverse chemical space. You can read more about their performance on benchmarks in Reference (1) cited below.<\/p>\n\n\n\n

The platform employs a combination of random forest models trained on extended-connectivity fingerprints (ECFP)and gradient-boosted decision trees (GBDT) that leverage over 200 molecular descriptors \u2014 from topological indices and polar surface area to atom-level physicochemical features. This hybrid approach enables MapLight to capture both structural similarity patterns and nonlinear feature relationships, leading to predictive accuracy across a wide variety of ADMET endpoints.<\/p>\n\n\n\n

Within the\u00a0Therapeutic Data Commons (TDC)\u00a0Leadership Boards, MapLight\u2019s ADMET predictors rank among the\u00a0top three performing models\u00a0for key endpoints such as\u00a0P-glycoprotein (P-gp) inhibition\u00a0and\u00a0blood\u2013brain barrier (BBB) permeability. These properties are essential for understanding whether compounds can reach their target sites effectively or pose drug-drug interaction risks.<\/p>\n\n\n\n

Incorporating MapLight into\u00a0BMaps\u00a0provides users with\u00a0seamless ADMET prediction capabilities\u00a0alongside existing design and scoring workflows. Over 20 different ADMET properties are automatically predicted as compounds are imported alongside their crystal protein, or else created in the BMaps platform. No matter what stage in the drug design process you are in, ADMET scoring can now be run in a single step. Examples of various views of ADMET properties shown in the figures below:<\/p>\n\n\n

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Figure 1<\/strong>: Workspace view of compound properties (select MapLight properties shown)<\/figcaption><\/figure><\/div>\n\n\n
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Figure 2<\/strong>: Compound Table view of compound and properties<\/figcaption><\/figure><\/div>\n\n
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Figure 3<\/strong>: Tooltip description of example property accessed via mouse hover.<\/figcaption><\/figure><\/div><\/div>\n\n\n\n

Why Does This Matter?<\/h2>\n\n\n\n

In silico design has long focused on potency \u2014 optimizing for how tightly a molecule binds to a target protein. But as every medicinal chemist knows, potent molecules aren\u2019t always good drugs. Issues like poor solubility, rapid metabolism, or efflux by transporters can derail otherwise promising leads.<\/p>\n\n\n\n

By integrating ADMET prediction directly into BMaps, we enable users to\u00a0prioritize compounds that balance potency with\u00a0druggability.<\/p>\n\n\n\n

This means:<\/p>\n\n\n\n