ReviewBioMed research international2019
Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery.
Review in BioMed research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules.Nature communications · 2025Article
- The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials.Pharmaceuticals (Basel, Switzerland) · 2025Review
- RETRACTED: A novel spectral transformation technique based on special functions for improved chest X-ray image classification.PloS one · 2025Article
- Natural AI-based drug designing by modification of ascorbic acid and curcumin to combat buprofezin toxicity by using molecular dynamics study.Scientific reports · 2024Article
- Artificial Intelligence for Precision Oncology of Triple-Negative Breast Cancer: Learning from Melanoma.Cancers · 2024Article
- The Identification of a Novel Spider Toxin Peptide, Lycotoxin-Pa2a, with Antibacterial and Anti-Inflammatory Activities.Antibiotics (Basel, Switzerland) · 2023Article
- The Benefits and Challenges of Antibiotics-Non-Steroidal Anti-Inflammatory Drugs Non-Covalent Reaction.Molecules (Basel, Switzerland) · 2023Review
- Review
- Insights into the structure and function of the histidine kinase ComP from Bacillus amyloliquefaciens based on molecular modeling.Bioscience reports · 2022Article
- Molecular dynamics simulations, docking and MMGBSA studies of newly designed peptide-conjugated glucosyloxy stilbene derivatives with tumor cell receptors.Molecular diversity · 2022Article
- Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.Molecular diversity · 2022Review
- An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.Medical physics · 2022Article
- Implications of prognosis-associated genes in pancreatic tumor metastasis: lessons from global studies in bioinformatics.Cancer metastasis reviews · 2021Review
- Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.Molecular diversity · 2021Article
- Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future.Cancer cell international · 2021Review
- Comprehensive Analysis of Prognostic and Genetic Signatures for General Transcription Factor III (GTF3) in Clinical Colorectal Cancer Patients Using Bioinformatics Approaches.Current issues in molecular biology · 2021Article
- Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models.Journal of ovarian research · 2020Article
- Artificial intelligence (AI) and big data in cancer and precision oncology.Computational and structural biotechnology journal · 2020Review
- The Use of Artificial Intelligence for Complete Cytoreduction Prediction in Epithelial Ovarian Cancer: A Narrative Review.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into "usable" knowledge. Being well aware of this, the world's leading pharmaceutical companies have already begun to use artificial intelligence to improve their research regarding new drugs. The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources to aid drug development. Pharmaceutical and medical researchers have extensive data sets that can be analyzed by strong AI systems. This review focused on how computational biology and artificial intelligence technologies can be implemented by integrating the knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in the cancer precision drug discovery.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.