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Computational drug discovery is essential for screening potential treatments and reducing the costs and time associated with proposing or combining drugs for disease management. Despite the extensive research conducted in this field, it remains an emerging area, particularly with the advent of machine learning, deep learning, and large language models (LLMs). This systematic review examines the integration of machine learning and deep learning techniques in drug discovery, concentrating on three critical areas: drug−drug interactions (DDIs), drug-target interactions (DTIs), and adverse drug reactions (ADRs). The review analyzes over 100 papers published between 2020 and 2025, categorizing the methods into deep learning, machine learning, graph learning, and hybrid models. It highlights the transformative impact of natural language processing (NLP) and LLMs in extracting meaningful insights from biomedical literature and chemical data. Furthermore, this work introduces key databases and data sets widely utilized in drug discovery. Additionally, this review identifies gaps in the existing research, such as the lack of comprehensive studies that simultaneously address DDI, DTI, and ADR extraction, and it proposes a more holistic approach to fill these gaps. The paper concludes by thoroughly evaluating various models, underscoring their performance metrics. Show less

Computational modeling has been adopted in all aspects of drug research and development, from the early phases of target identification and drug discovery to the late-stage clinical trials. The different questions addressed during each stage of drug R&D has led to the emergence of different modeling methodologies. In the research phase, systems biology couples experimental data with elaborate computational modeling techniques to capture lifecycle and effector cellular functions (e.g. metabolism, signaling, transcription regulation, protein synthesis and interaction) and integrates them in quantitative models. These models are subsequently used in various ways, i.e. to identify new targets, generate testable hypotheses, gain insights on the drug’s mode of action (MOA), translate preclinical findings, and assess the potential of clinical drug efficacy and toxicity. In the development phase, pharmacokinetic/pharmacodynamic (PK/PD) modeling is the established way to determine safe and efficacious doses for testing at increasingly larger, and more pertinent to the target indication, cohorts of subjects. First, the relationship between drug input and its concentration in plasma is established. Second, the relationship between this concentration and desired or undesired PD responses is ascertained. Recognizing that the interface of systems biology with PK/PD will facilitate drug development, systems pharmacology came into existence, combining methods from PK/PD modeling and systems engineering explicitly to account for the implicated mechanisms of the target system in the study of drug–target interactions. Herein, a number of popular system biology methodologies are discussed, which could be leveraged within a systems pharmacology framework to address major issues in drug development. © 2013 The Authors. Biopharmaceutics & Drug Disposition published by John Wiley & Sons, Ltd. Key words: systems pharmacology; systems biology; modeling of signaling pathways; identification of drug mode of action; prediction of clinical drug efficacy Show less