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Drug-target affinity model performance varies with evaluation shifts

By PulseAugur Editorial · [1 sources] ·

A new research paper explores how different evaluation methods for drug-target affinity (DTA) models can lead to varying conclusions about model performance. The study found that when models are tested on data distributions that differ from their training data in specific ways (e.g., new chemical series or new protein targets), the perceived best architecture can change significantly. This highlights the importance of aligning evaluation strategies with the expected real-world application of DTA models. AI

IMPACT Highlights the need for robust evaluation methods in AI models, particularly in scientific applications like drug discovery.

RANK_REASON Research paper detailing methodology for evaluating machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Drug-target affinity model performance varies with evaluation shifts

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Research paper detailing methodology for evaluating machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Minjae Chung, Clara Li, Malar Paavai Muthukumaran, Shaunna Wang, Aniket Ramkrishnan Iyer, Shaun Qien Yeau Tan, Harinishree Sathu, Micky C. Nnamdi, J. Ben Tamo, Benoit Louis Marteau, May Dongmei Wang ·

    Beyond Random Splits: Evaluating Drug-Target Affinity Models Under Chemically and Biologically Motivated Distribution Shifts Copy

    arXiv:2610.03456v1 Announce Type: new Abstract: Drug-target affinity (DTA) prediction is widely used to prioritize candidate compounds before costly experimental screening. DTA models are often compared under a single data split, even though deployment may require extrapolation t…

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