Using attribution to decode binding mechanism in neural network models for chemistry Skip to main content Explore our many areas of focus Explore all research areas Applied AI & sciences Earth AI Health AI Science AI Sustainability & crisis resilience Foundational ML & algorithms Algorithms & theory Information retrieval Machine intelligence Machine perception Natural language processing People, systems & quantum AI Human-computer interaction and visualization Networking Quantum AI Responsible AI Anti abuse Software engineering Software systems Learn More Publications Projects Building a collaborative ecosystem Datasets Access high-quality datasets to accelerate your research. Tools & services Explore our latest AI models and products. Open source Discover open-source code and collaborate with the community. Shaping the future together See all programs Faculty programs Participating in the academic research community through meaningful engagement with university faculty. 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Google Research Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Research Resources Conferences & events Careers Blog About Search Home Publications Using attribution to decode binding mechanism in neural network models for chemistry Kevin McCloskey Ankur Taly Federico Monti Michael Brenner Lucy Colwell Proceedings of the National Academy of Sciences (2019), pp. 201820657 Download Google Scholar Copy Bibtex Abstract Deep neural networks have achieved state of the art accuracy at classifying molecules with respect to whether they bind to specific protein targets. A key breakthrough would occur if these models could reveal the fragment pharmacophores that are causally involved in binding. Extracting chemical details of binding from the networks could potentially lead to scientific discoveries about the mechanisms of drug actions. But doing so requires shining light into the black box that is the trained neural network model, a task that has proved difficult across many domains. Here we show how the binding mechanism learned by deep neural network models can be interrogated, using a recently described attribution method. We first work with carefully constructed synthetic datasets, in which the 'fragment logic' of binding is fully known. We find that networks that achieve perfect accuracy on held out test datasets still learn spurious correlations due to biases in the datasets, and we are able to exploit this non-robustness to construct adversarial examples that fool the model. The dataset bias makes these models unreliable for accurately revealing information about the mechanisms of protein-ligand binding. In light of our findings, we prescribe a test that checks for dataset bias given a hypothesis. If the test fails, it indicates that either the model must be simplified or regularized and/or that the training dataset requires augmentation. Research Areas Machine intelligence Meet the teams driving innovation Our teams advance the state of the art through research, systems engineering, and collaboration across Google. See our teams Follow us Explore our other initiatives Google AI Discover how Google AI is committed to enriching knowledge and solving complex challenges Products Build Research Responsibility Societal Impact About Google Cloud High-performance infrastructure for cloud computing, data analytics & machine learning Overview Solutions Products Pricing Resources Google DeepMind Our mission is to build AI responsibly to benefit humanity Models Research Science About Google Labs Explore the future of AI responsibly with Google Labs About Experiments Stay connected About Google Google Products Privacy Terms Cookies management controls ×