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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

Developing novel therapeutics often follows three steps: target identification, design of strategies to suppress target activity and drug development to implement the strategies. In this review, we recount the evidence identifying the basic leucine zipper transcription factors ATF5, CEBPB, and CEBPD as targets for brain and other malignancies. We describe strategies that exploit the structures of the three factors to create inhibitory dominant-negative (DN) mutant forms that selectively suppress growth and survival of cancer cells. We then discuss and compare four peptides (CP-DN-ATF5, Dpep, Bpep and ST101) in which DN sequences are joined with cell-penetrating domains to create drugs that pass through tissue barriers and into cells. The peptide drugs show both efficacy and safety in suppressing growth and in the survival of brain and other cancers in vivo, and ST101 is currently in clinical trials for solid tumors, including GBM. We further consider known mechanisms by which the peptides act and how these have been exploited in rationally designed combination therapies. We additionally discuss lacunae in our knowledge about the peptides that merit further research. Finally, we suggest both short- and long-term directions for creating new generations of drugs targeting ATF5, CEBPB, CEBPD, and other transcription factors for treating brain and other malignancies. Citation: Greene, L.A.; Zhou, Q.; Siegelin, M.D.; Angelastro, J.M. Targeting Transcription Factors ATF5, Show less

The phosphatidylinositol-3 kinase (PI3K)-AKT pathway is one of the most commonly dysregulated pathways in all of cancer, with somatic mutations, copy number alterations, aberrant epigenetic regulation and increased expression in a number of cancers. The carefully maintained homeostatic balance of cell division and growth on one hand, and programmed cell death on the other, is universally disturbed in tumorigenesis, and downstream effectors of the PI3K-AKT pathway play an important role in this disturbance. With a wide array of downstream effectors involved in cell survival and proliferation, the well-characterized direct interactions of AKT make it a highly attractive yet elusive target for cancer therapy. Here, we review the salient features of this pathway, evidence of its role in promoting tumorigenesis and recent progress in the development of therapeutic agents that target AKT. 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