From interpretive expertise to multimodal analytical inference Summary Determining a molecule’s configuration, or resolving its enantiomers on a column, has traditionally meant comparing experimental data against a computed reference a process gated by quantum chemistry and by expert judgement. Machine learning is now compressing that comparison from hours to seconds and, in chiral chromatography, is …
Why chirality has to be built into a molecular representation before any model can learn it Episode 1 of 5 Summary Two enantiomers can differ by an order of magnitude in potency, yet to most molecular machine learning models they are the same molecule. This is not a data problem that more training examples will …
Episode 1: Teaching Machines Molecular Handedness Read More »
A chiral perspective on how we describe, teach, and encode molecular handedness in the age of artificial intelligence Prelude Chiral molecules are not flat drawings on paper (2D). They live in space (3D). They twist, reflect, and occupy three dimensions in ways that decide how they bind to receptors, how they are metabolized, and ultimately …
Can an AI truly understand a molecule if it cannot tell left from right? Artificial intelligence now shapes the earliest and most consequential stages of drug discovery, from virtual screening and ADMET prediction to generative molecular design. Yet many of these systems operate in an effectively achiral digital environment, where molecules are represented as flat …
Teaching Machines to See in 3D: The Chiral Blind Spot in AI-Driven Drug Discovery Read More »
❓ Can AI truly be intelligent if it is stereo-blind? 🌀 On Visuals and Stereochemical TruthSome illustrations in this series are generated or assisted by AI to support conceptual understanding. These visuals are intentionally simplified and should not be read as stereochemically rigorous or chemically exact representations. Wherever stereochemical fidelity matters, it is addressed explicitly …
Episode 5: Beyond Bias: Toward Chiral Intelligence Read More »
❓ How do we move from recognizing chiral bias to responsibly fixing it? On Visuals and Stereochemical TruthSome illustrations in this series are generated or assisted by AI to support conceptual understanding. These visuals are intentionally simplified and should not be read as stereochemically rigorous or chemically exact representations. Wherever stereochemical fidelity matters, it is …
Episode 4: Fixing the Bias: Data, Models and Generative Constraints Read More »
“When molecules have two hands but AI only sees one.” On Visuals and Stereochemical TruthSome illustrations in this series are generated or assisted by AI to support conceptual understanding. These visuals are intentionally simplified and should not be read as stereochemically rigorous or chemically exact representations. Wherever stereochemical fidelity matters, it is addressed explicitly in …
Episode 3: When Algorithms Miss the Mirror: How Chiral Blind Spots Break the Pipeline? Read More »
“AI isn’t blind—just stereo-sloppy” On Visuals and Stereochemical TruthSome illustrations in this series are generated or assisted by AI to support conceptual understanding. These visuals are intentionally simplified and should not be read as stereochemically rigorous or chemically exact representations. Wherever stereochemical fidelity matters, it is addressed explicitly in the discussion—because in chemistry, especially in …
“When algorithms forget the mirror, molecules lose their meaning” On Visuals and Stereochemical TruthSome illustrations in this series are generated or assisted by AI to support conceptual understanding. These visuals are intentionally simplified and should not be read as stereochemically rigorous or chemically exact representations. Wherever stereochemical fidelity matters, it is addressed explicitly in the …
Episode 1: The Promise and the Paradox: AI That Miss the Mirror Read More »