@caithmac

I will build in public! What tho? My life. PhD Computer Science (ongoing) Computer Vision,ML/DL, Philosophy,History.

caithmac.vercel.app/
Joined March 2024
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109Media
We just published our work on an explainable active learning framework for ligand–protein binding affinity prediction in Digital Discovery. 🔗 pubs.rsc.org/en/content/arti… Here’s a quick breakdown of what we did and why 👇

Explainable active learning framework for ligand binding affinity prediction

Active learning (AL) prioritises which compounds to measure next for protein–ligand affinity when assay or simulation budgets are limited. We present an ex

pubs.rsc.org
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We evaluate the framework across multiple settings and compare against standard baselines. Key takeaway: We can maintain (or improve) performance while gaining explainability.
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One interesting observation: The samples selected by our method are not just “uncertain” they often correspond to meaningful interaction patterns. This gives more confidence in the active learning loop.
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SHAP analysis revealed chemically meaningful features driving predictions. The model learns to focus on SAR-relevant motifs over time. We identified key fragments for high affinity (e.g., halogens for TYK2).
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This is not a final solution, but a step toward: • More transparent ML models • Better human–AI collaboration in science • Active learning systems that scientists can actually trust
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Big thanks to all collaborators and reviewers who helped improve the work. @gorantlarohan @ppxasjsm If you’re working on: • drug discovery • active learning • explainable ML we’d love to hear your thoughts!
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Instead of just predicting affinity, the model provides insight into: 👉 Which parts of the ligand and protein matter 👉 Why a sample is selected during active learning This helps move from “prediction” → “understanding”.
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Predicting binding affinity is central to drug discovery, but data is expensive. Active learning helps by selecting which experiments to run next, instead of blindly collecting more data. But there’s a problem…
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Most active learning methods are black boxes. They may pick good samples, but don’t tell us why those samples are useful. In drug discovery, that lack of interpretability is a real limitation.
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Our goal was simple: 👉 Build an active learning framework that is not only effective 👉 But also explainable So that model decisions can be inspected, trusted, and potentially acted upon.
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Explainable Active Learning Framework for Ligand BindingAffinity Prediction biorxiv.org/content/10.64898… #biorxiv_biophys

Explainable Active Learning Framework for Ligand Binding Affinity Prediction

Active learning (AL) prioritises which compounds to measure next for protein–ligand affinity when assay or simulation budgets are limited. We present an explainable AL framework built on Gaussian...

biorxiv.org
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As soon as I tell Gemini that this was my paper. I get praised instead of criticism. Lol!
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If anyone wants to take the interpretability test.
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Okay, I will be back in sometime! @NotebookLM really outdone themselves.
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Reviewer 1: “excellent” Reviewer 3: “really strong stuff” Reviewer 2:
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Finally can relate.
Hugh Kearns@ithinkwellHugh31 Oct 2025
#Halloween2025 Really scary.
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always ❤️ the idea of D&D party debates but never learned the rules, so I built my own AI-powered RPG to capture that magic! It features two AI agents with opposite personalities Honestly, I just want to be a player in it and see what happens next!
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Right now the narrator is pretty simple and it's a single stage, but the plan is to add memory so their decisions actually carry consequences.
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Aeron accepts Liana's suggestions btw.
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