Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language

• URL: http://arxiv.org/abs/2303.03363v2
• Date: Fri, 16 Jun 2023 09:59:34 GMT
• Title: Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
• Authors: Philipp Seidl, Andreu Vall, Sepp Hochreiter, G\"unter Klambauer
• Abstract summary: We envision a novel type of activity prediction model that is able to adapt to new prediction tasks at inference time. Our method CLAMP yields improved predictive performance on few-shot learning benchmarks and zero-shot problems in drug discovery.
• Score: 5.117101148161245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Activity and property prediction models are the central workhorses in drug discovery and materials sciences, but currently they have to be trained or fine-tuned for new tasks. Without training or fine-tuning, scientific language models could be used for such low-data tasks through their announced zero- and few-shot capabilities. However, their predictive quality at activity prediction is lacking. In this work, we envision a novel type of activity prediction model that is able to adapt to new prediction tasks at inference time, via understanding textual information describing the task. To this end, we propose a new architecture with separate modules for chemical and natural language inputs, and a contrastive pre-training objective on data from large biochemical databases. In extensive experiments, we show that our method CLAMP yields improved predictive performance on few-shot learning benchmarks and zero-shot problems in drug discovery. We attribute the advances of our method to the modularized architecture and to our pre-training objective.

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