Chapter 2 - General considerations on artificial intelligence

Abstract

Machine learning is a subset of the umbrella term artificial intelligence (AI). AI has already crept into several tasks of our day-to-day life, like digital assistants, internet surfing, online shopping, etc. Machine learning (ML), as the name indicates, is a way (algorithm) of self-learning by computer. The development of ML algorithms originated from the quest of computers that learn on their own based on their experiences. The learning takes place with the help of a dataset provided to the computer as training data. It basically helps in decision making or prediction of an outcome when the situation is having manifold factors and when decision making is not straightforward as per human intelligence. Drug discovery and delivery is a complicated process requiring a lot of human aptitudes and decision-making ability. The process is characterized by abundant data handling with multiple variables, thus making it amenable to the application of ML. Opportunities for the application of ML occur at nearly all stages of drug discovery, like target identification and validation, compound screening, lead identification and optimization, preclinical development, clinical trials, and biomarker identification and analysis. However, for the effective application of ML, its basic understanding is inevitable. The knowledge and technology about ML in healthcare are advancing considerably. Various software libraries are available online that can work with a range of hardware, even simple personal computers. Proper understanding and selection of an appropriate machine learning approach may provide accurate predictions. This chapter will provide various ML approaches and their areas of applications with suitable examples. Several ambiguities in the available methods of ML are cropping up as these are being applied to actual situations in the healthcare sector. However, scientists are also coming up with new techniques better suited to the area of healthcare. Deep learning is an approach apt for complex drug discovery data. However, the level of the algorithm to be generated is more complex in this approach. Another challenge in the application of ML to drug discovery is the availability of sufficient, accurate data to be fed for training. Generation of data itself may be a costly affair in certain phases of drug development. Although, there are few bottlenecks still to be resolved before ML can be applied full-fledged in drug discovery. The lack of repeatability and interpretability of the data generated by ML is posing a challenge for its accountability and reliability in different situations like the approval processes and IPR. There are methods that are proposed by scientists to improve the fitting of ML models and improve its output data, but, a great deal of work is required to be done in this area. The purpose of this chapter is to provide a basic understanding of ML concepts. This chapter is expected to bring more clarity to the potential applications of ML in drug design and development. We anticipate providing an overall view of the ML methods, their applications, and limitations so that aspirant researchers can be benefited.