{'fraction_non_alphanumeric': 0.01633003867640739, 'fraction_numerical': 0.0030081650193382035, 'mean_word_length': 5.970059880239521, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Classic Artificial Intelligence: Tools for Autonomous Reasoning Adaptive Evolution of Peptide Inhibitors for Mutating SARS-CoV-2

Summary Autonomous reasoning requires that the cognitive entity perceives relationships amongst data elements and makes inferences about these elements and their relations to subsequently select the appropriate action This chapter summarizes some of the available techniques for achieving autonomous reasoning Essentially, it presents a toolbox of classic artificial intelligence techniques that could be useful for automating inference tasks in networks For each tool or technique, the presentation includes an evaluation of the accorded degree of cognition based on the cognitive decision-making model The chapter provides the basis on which the specific tool may be selected for application towards specific network challenges It focuses on the most common methods that are also likely to have wider usage in network management: expert systems, closed-loop control systems, case-based reasoning, and fuzzy inference systems These methods are presented in an order that highlights ever more cognitive capability beyond simple inference Abstract The SARS-CoV-2 virus is currently causing a worldwide pandemic with dramatic societal consequences for the humankind In the past decades, disease outbreaks due to such zoonotic pathogens have appeared with an accelerated rate, which calls for an urgent development of adaptive (smart) therapeutics Here, a computational strategy is developed to adaptively evolve peptides that could selectively inhibit mutating S protein receptor binding domains (RBDs) of different SARS-CoV-2 viral strains from binding to their human host receptor, angiotensin-converting enzyme 2 (ACE2) Starting from suitable peptide templates, based on selected ACE2 segments (natural RBD binder), the templates are gradually modified by random mutations, while retaining those mutations that maximize their RBD-binding free energies In this adaptive evolution, atomistic molecular dynamics simulations of the template-RBD complexes are iteratively perturbed by the peptide mutations, which are retained under favorable Monte Carlo decisions The computational search will provide libraries of optimized therapeutics capable of reducing the SARS-CoV-2 infection on a global scale