AIMC Topic: Drug Discovery

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Drug-target interaction prediction using reliable negative samples and effective feature selection methods.

Journal of pharmacological and toxicological methods
Machine learning-based approaches in the field of drug discovery have dramatically reduced the time and cost of the laboratory process of detecting potential drug-target interactions (DTIs). Standard binary classifiers require both positive and negat...

Exploring Low-Toxicity Chemical Space with Deep Learning for Molecular Generation.

Journal of chemical information and modeling
Creating a wide range of new compounds that not only have ideal pharmacological properties but also easily pass long-term toxicity evaluation is still a challenging task in current drug discovery. In this study, we developed a conditional generative ...

Systems Drug Discovery for Diffuse Large B Cell Lymphoma Based on Pathogenic Molecular Mechanism via Big Data Mining and Deep Learning Method.

International journal of molecular sciences
Diffuse large B cell lymphoma (DLBCL) is an aggressive heterogeneous disease. The most common subtypes of DLBCL include germinal center b-cell (GCB) type and activated b-cell (ABC) type. To learn more about the pathogenesis of two DLBCL subtypes (i.e...

Empowering the discovery of novel target-disease associations via machine learning approaches in the open targets platform.

BMC bioinformatics
BACKGROUND: The Open Targets (OT) Platform integrates a wide range of data sources on target-disease associations to facilitate identification of potential therapeutic drug targets to treat human diseases. However, due to the complexity that targets ...

Maximizing the integration of virtual and experimental screening in hit discovery.

Expert opinion on drug discovery
INTRODUCTION: Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approac...

Organic Compound Synthetic Accessibility Prediction Based on the Graph Attention Mechanism.

Journal of chemical information and modeling
Accurate estimation of the synthetic accessibility of small molecules is needed in many phases of drug discovery. Several expert-crafted scoring methods and descriptor-based quantitative structure-activity relationship (QSAR) models have been develop...

QMugs, quantum mechanical properties of drug-like molecules.

Scientific data
Machine learning approaches in drug discovery, as well as in other areas of the chemical sciences, benefit from curated datasets of physical molecular properties. However, there currently is a lack of data collections featuring large bioactive molecu...

Retro Drug Design: From Target Properties to Molecular Structures.

Journal of chemical information and modeling
To deliver more therapeutics to more patients more quickly and economically is the ultimate goal of pharmaceutical researchers. The advent and rapid development of artificial intelligence (AI), in combination with other powerful computational methods...