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 Show more
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 re Show more
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