Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_117_библиотеки_им_акад_М_И_Перельмана

The ever-evolving advancements in deep learning are currently assisting several elds of the chemical sciences [11–13]. In the past few years, several “articial intelligence” (AI) principles have been successfully used to computerised drug dis­covery [11, 12, 14]. The ability of advanced learning algorithms to simulate com­plex non-linear input-output relationships, perform pattern recognition, and extract features from low-level data representations is primarily responsible for this growth. Articial neural networks with several processing layers are what deep learning algorithms are made of. Some deep learning models have been shown to outperform well-known machine learning and quantitative structure-activity relationship (QSAR) methods for drug development [15–17]. Deep learning has further expanded the potential and utility of computer-assisted discovery, for example, in the elds of protein structure prediction, macromolecular target identication, molecule design, and chemical synthesis planning [18–21]. A model’s limited understanding fre­quently pays for the ability to capture complicated non-linear relationships between the input data (such as chemical structure representations) and the corresponding outputs (such as assay results). While efforts are currently made to clarify QSARs using computational insights and molecular descriptor analysis [22–27], models of deep neural networks are infamous for being inaccessible to the human mind right away [28]. The abundance of “rules of thumb” linking biological effects with physi­cochemical qualities, particularly in medicinal chemistry, highlights the readiness to sometimes trade precision in favour of models that better match human intuition [29, 30]. With the recent resurgence of neural networks in chemistry and medicine, automated analysis of medical and chemical information to extract and depict char­acteristics in a human-intelligible manner has been around since 1990s [31, 32].

There will be a greater demand for techniques that help in understanding and interpreting the models that underneath given the speed at which AI is being applied in drug development and related domains. Emphasis has been focused on explain­able AI (XAI) techniques in an attempt to reduce the uninterpretability of some machine learning models and to improve human reasoning and decision-making [33, 34]. Establishing the fundamental decision-making process transparent (“understandable”) avoiding making precise forecasts for the incorrect causes (the so-called clever Hans effect) [35], preventing unfair biases or unlawful injustice,