Informatics
The practicalities of deploying AI Beneath this big picture change, there are a number of practicalities of deploying AI which also need to be understood. Each AI deployment will need pragmatic under-
standing of the intended real-world usage, and must be driven by people who understand the data and the underlying issue being solved, not detached technology professionals. While some niche tools with narrow applications can be simply plugged into your organisation, most AI solutions will need to be custom built to solve very specific problems and trained on your own data. The AI should be robust by design, selecting or creating the right tools and algorithms for the task. Explainability requirements must also be consid-
ered in design. If the user does not understand how AI works, they will struggle to trust the results. Fair, safe systems that can be trusted may require some level of explainable AI (XAI), the principle of designing systems with provision for interpretation and understanding of decisions. This is an actively developing research area, but through a considered
approach to data, users and the system, creation of trustworthy automated systems is not an insur- mountable challenge. Even so, in some cases it will just be too compli-
cated for humans to fully understand how an AI reached its decision; trust can only be built through careful validation and testing. For the language translation project, we set aside molecules and reduced graphs from the training data, which were used to provide reduced graphs of drug candidates from published literature which the system had never seen before. If the AI system could take these high-level descriptions and generate a known active compound, this would be a great indication of its value in future discovery programmes. We performed this test with several different known active molecules, which had not been seen by the AI system. In most cases, a known active com- pound was generated. Finally, models must be subject to ongoing mon-
itoring through a skilled operations team, with adequate long-term support, retraining processes and controls for model drift over time.
Drug Discovery World Fall 2019
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