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
2025 · Nucleic acids research · Oxford University Press · added 2026-04-21
One of the major challenges in precision oncology is the identification of pathogenic, actionable variants and the selection of personalized treatments. We present Onkopus, a variant interpretation fr Show more
One of the major challenges in precision oncology is the identification of pathogenic, actionable variants and the selection of personalized treatments. We present Onkopus, a variant interpretation framework based on a modular architecture, for interpreting and prioritizing genetic alterations in cancer patients. A multitude of tools and databases are integrated into Onkopus to provide a comprehensive overview about the consequences of a variant, each with its own semantic, including pathogenicity predictions, allele frequency, biochemical and protein features, Show less
The emergence and continued global spread of the current COVID-19 pandemic has highlighted the need for methods to identify novel or repurposed therapeutic drugs in a fast and effective way. Despite t Show more
The emergence and continued global spread of the current COVID-19 pandemic has highlighted the need for methods to identify novel or repurposed therapeutic drugs in a fast and effective way. Despite the availability of methods for the discovery of antiviral drugs, the majority tend to focus on the effects of such drugs on a given virus, its constituent proteins, or enzymatic activity, often neglecting the consequences on host cells. This may lead to partial assessment of the efficacy of the tested anti-viral compounds, as potential toxicity impacting the overall physiology of host cells may mask the effects of both viral infection and drug candidates. Here we present a method able to assess the general health of host cells based on morphological profiling, for untargeted phenotypic drug screening against viral infections. Show less