ArticleMolecular diversity2017
Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins.
Article in Molecular diversity, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
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- AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules.Pharmaceuticals (Basel, Switzerland) · 2024Article
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- VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search.Journal of chemical information and modeling · 2023Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha.Biomolecules · 2021Article
- Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine.Cancer communications (London, England) · 2021Review
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery.International journal of molecular sciences · 2021Article
- QSAR Modeling for Multi-Target Drug Discovery: Designing Simultaneous Inhibitors of Proteins in Diverse Pathogenic Parasites.Frontiers in chemistry · 2021Article
- A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing.Pharmaceuticals (Basel, Switzerland) · 2020Article
- Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds.ACS omega · 2020Article
- Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria.Journal of chemical information and modeling · 2020Article
- A Review on Applications of Computational Methods in Drug Screening and Design.Molecules (Basel, Switzerland) · 2020Review
- Construction of Quantitative Structure Activity Relationship (QSAR) Models to Predict Potency of Structurally Diversed Janus Kinase 2 Inhibitors.Molecules (Basel, Switzerland) · 2019Article
- Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents.Molecules (Basel, Switzerland) · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
Breast cancer is the most frequent cancer reported in women, being responsible for hundreds of thousands of deaths. Chemotherapy has proven to be effective against this malignant neoplasm depending on different biological factors such as the histopathology, grade, and stage, among others. However, breast cancer cells have become resistant to current chemotherapeutic regimens, urging the discovery of new anti-breast cancer drugs. Computational approaches have the potential to offer promising alternatives to accelerate the search for potent and versatile anti-breast cancer agents. In the present work, we introduce the first multitasking (mtk) computational model devoted to the in silico fragment-based design of new molecules with high inhibitory activity against 19 different proteins involved in breast cancer. The mtk-computational model was created from a dataset formed by 24,285 cases, and it exhibited accuracy around 93% in both training and prediction (test) sets. Several molecular fragments were extracted from the molecules present in the dataset, and their quantitative contributions to the inhibitory activities against all the proteins under study were calculated. The combined use of the fragment contributions and the physicochemical interpretations of the different molecular descriptors in the mtk-computational model allowed the design of eight new molecular entities not reported in our dataset. These molecules were predicted as potent multi-target inhibitors against all the proteins, and they exhibited a desirable druglikeness according to the Lipinski's rule of five and its variants.
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Registered trials
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