AIMC Topic: Molecular Targeted Therapy
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Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets.
Stroke and vascular neurology
Mar 29, 2020
The discovery of targeted drugs heavily relies on three-dimensional (3D) structures of target proteins. When the 3D structure of a protein target is unknown, it is very difficult to design its corresponding targeted drugs. Although the 3D structures ...
Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma.
Nature reviews. Gastroenterology & hepatology
Jan 3, 2020
Hepatocellular carcinoma (HCC) is the most common form of primary adult liver cancer. After nearly a decade with sorafenib as the only approved treatment, multiple new agents have demonstrated efficacy in clinical trials, including the targeted thera...
Artificial Intelligence Steering Molecular Therapy in the Absence of Information on Target Structure and Regulation.
Journal of chemical information and modeling
Dec 4, 2019
Protein associations are at the core of biological activity, and the drug-based disruption of dysfunctional associations poses a major challenge to targeted therapy. The problem becomes daunting when the structure and regulated modulation of the comp...
Machine learning for target discovery in drug development.
Current opinion in chemical biology
Nov 14, 2019
The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursu...
Artificial intelligence and big data facilitated targeted drug discovery.
Stroke and vascular neurology
Nov 7, 2019
Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database in...
Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation.
Journal of chemical information and modeling
Sep 6, 2019
We propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. We introduce a distance-aware graph attention algorithm to differentiate various types of intermolecular interactions. Furthermore, we extr...
Accurate prediction of potential druggable proteins based on genetic algorithm and Bagging-SVM ensemble classifier.
Artificial intelligence in medicine
Jul 19, 2019
Discovering and accurately locating drug targets is of great significance for the research and development of new drugs. As a different approach to traditional drug development, the machine learning algorithm is used to predict the drug target by min...
Future of Radiotherapy in Nasopharyngeal Carcinoma.
The British journal of radiology
Jul 9, 2019
Nasopharyngeal carcinoma (NPC) is a malignancy with unique clinical biological profiles such as associated Epstein-Barr virus infection and high radiosensitivity. Radiotherapy has long been recognized as the mainstay for the treatment of NPC. However...
Error Tolerance of Machine Learning Algorithms across Contemporary Biological Targets.
Molecules (Basel, Switzerland)
Jun 4, 2019
Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficacy of machine learning in these arenas is reliant upon highly accurate and abundant data. These two l...
Deep Learning to Therapeutically Target Unreported Complexes.
Trends in pharmacological sciences
May 21, 2019
The disruption of large protein-protein (PP) interfaces remains a challenge in targeted therapy. Designing drugs that compete with binding partners is daunting, especially when the structure of the protein complex is unknown. To address the problem w...
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