ReviewDrug discovery today2017
From flamingo dance to (desirable) drug discovery: a nature-inspired approach.
Review in Drug discovery today, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Application of Data-Centric Supervised Machine Learning to Predict Phenotypic Activity Against Clinically Relevant Stages ofPharmaceutics · 2025Article
- Beyond Amyloid: A Machine Learning-Driven Approach Reveals Properties of Potent GSK-3β Inhibitors Targeting Neurofibrillary Tangles.International journal of molecular sciences · 2024Article
- QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency.Pharmaceutics · 2023Article
- Ayurveda and in silico Approach: A Challenging Proficient Confluence for Better Development of Effective Traditional Medicine Spotlighting Network Pharmacology.Chinese journal of integrative medicine · 2023Review
- Reinforcement learning for systems pharmacology-oriented and personalized drug design.Expert opinion on drug discovery · 2022Review
- Machine intelligence-driven framework for optimized hit selection in virtual screening.Journal of cheminformatics · 2022Article
- Article
- A method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria.Statistical methods in medical research · 2021Article
- QSAR Models for Active Substances againstMolecules (Basel, Switzerland) · 2021Article
- Advances in de Novo Drug Design: From Conventional to Machine Learning Methods.International journal of molecular sciences · 2021Review
- A desirability-based multi objective approach for the virtual screening discovery of broad-spectrum anti-gastric cancer agents.PloS one · 2018Article
- Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals.Journal of chemical information and modeling · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
The therapeutic effects of drugs are well known to result from their interaction with multiple intracellular targets. Accordingly, the pharma industry is currently moving from a reductionist approach based on a 'one-target fixation' to a holistic multitarget approach. However, many drug discovery practices are still procedural abstractions resulting from the attempt to understand and address the action of biologically active compounds while preventing adverse effects. Here, we discuss how drug discovery can benefit from the principles of evolutionary biology and report two real-life case studies. We do so by focusing on the desirability principle, and its many features and applications, such as machine learning-based multicriteria virtual screening.
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.