DebiasedDTA: Model Debiasing to Boost Drug -- Target Affinity Prediction
- URL: http://arxiv.org/abs/2107.05556v1
- Date: Sun, 4 Jul 2021 19:21:37 GMT
- Title: DebiasedDTA: Model Debiasing to Boost Drug -- Target Affinity Prediction
- Authors: R{\i}za \"Oz\c{c}elik, Alperen Ba\u{g}, Berk At{\i}l, Arzucan
\"Ozg\"ur, Elif \"Ozk{\i}r{\i}ml{\i}
- Abstract summary: We present DebiasedDTA, the first model debiasing approach that avoids dataset biases in order to boost the affinity prediction on novel biomolecules.
The results show that DebiasedDTA can boost models while predicting the interactions between novel biomolecules.
The experiments also show that DebiasedDTA can augment the DTA prediction models of different input and model structures.
- Score: 0.10499611180329804
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Motivation: Computational models that accurately identify high-affinity
protein-compound pairs can accelerate drug discovery pipelines. These models
aim to learn binding mechanics through drug-target interaction datasets and use
the learned knowledge while predicting the affinity of any protein-compound
pair. However, the datasets they rely on bear misleading patterns that bias
models towards memorizing dataset-specific biomolecule properties, instead of
learning binding mechanics. Insufficiently focused on the binding mechanics,
the resulting models struggle while predicting the drug-target affinities
(DTA), especially between de novo biomolecules. Here we present DebiasedDTA,
the first model debiasing approach that avoids dataset biases in order to boost
the affinity prediction on novel biomolecules. DebiasedDTA uses ensemble
learning and weight sample adaptation for bias identification and avoidance and
is applicable to almost all existing DTA prediction models. Results: The
results show that DebiasedDTA can boost models while predicting the
interactions between novel biomolecules. Known biomolecules also benefit from
the performance boost, though the boost is amplified as the test biomolecules
become more dissimilar to the training set. The experiments also show that
DebiasedDTA can augment the DTA prediction models of different input and model
structures and can avoid biases of different sources. Availability: The source
code, the models, and the data sets are available at
https://github.com/boun-tabi/debiaseddta-reproduce Contact:
arzucan.ozgur@boun.edu.tr, elif.ozkirimli@roche.com
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