Graph convolutional networks for computational drug development and discovery.

Despite the fact that deep learning has achieved remarkable success in various domains over the past decade, its application in molecular informatics and drug discovery is still limited. Recent advances in adapting deep architectures to structured data have opened a new paradigm for pharmaceutical research. In this survey, we provide a systematic review on the emerging field of graph convolutional networks and their applications in drug discovery and molecular informatics. Typically we are interested in why and how graph convolution networks can help in drug-related tasks. We elaborate the existing applications through four perspectives: molecular property and activity prediction, interaction prediction, synthesis prediction and de novo drug design. We briefly introduce the theoretical foundations behind graph convolutional networks and illustrate various architectures based on different formulations. Then we summarize the representative applications in drug-related problems. We also discuss the current challenges and future possibilities of applying graph convolutional networks to drug discovery.

A machine learning and network framework to discover new indications for small molecules.

Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinson's disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts.

Artificial Intelligence in Cancer Research and Precision Medicine.

Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical care. Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has led to an explosion of AI use in various aspects of oncology research. These applications range from detection and classification of cancer, to molecular characterization of tumors and their microenvironment, to drug discovery and repurposing, to predicting treatment outcomes for patients. As these advances start penetrating the clinic, we foresee a shifting paradigm in cancer care becoming strongly driven by AI. SIGNIFICANCE: AI has the potential to dramatically affect nearly all aspects of oncology-from enhancing diagnosis to personalizing treatment and discovering novel anticancer drugs. Here, we review the recent enormous progress in the application of AI to oncology, highlight limitations and pitfalls, and chart a path for adoption of AI in the cancer clinic.

The Missing Pieces of Artificial Intelligence in Medicine.

Stakeholders across the entire healthcare chain are looking to incorporate artificial intelligence (AI) into their decision-making process. From early-stage drug discovery to clinical decision support systems, we have seen examples of how AI can improve efficiency and decrease costs. In this Opinion, we discuss some of the key factors that should be prioritized to enable the successful integration of AI across the healthcare value chain. In particular, we believe a focus on model interpretability is crucial to obtain a deeper understanding of the underlying biological mechanisms and guide further investigations. Additionally, we discuss the importance of integrating diverse types of data within any AI framework to limit bias, increase accuracy, and model the interdisciplinary nature of medicine. We believe that widespread adoption of these practices will help accelerate the continued integration of AI into our current healthcare framework.

Dopamine Receptor D5 is a Modulator of Tumor Response to Dopamine Receptor D2 Antagonism.

PURPOSE: Dopamine receptor D2 (DRD2) is a G protein-coupled receptor antagonized by ONC201, an anticancer small molecule in clinical trials for high-grade gliomas and other malignancies. DRD5 is a dopamine receptor family member that opposes DRD2 signaling. We investigated the expression of these dopamine receptors in cancer and their influence on tumor cell sensitivity to ONC201. EXPERIMENTAL DESIGN: The Cancer Genome Atlas was used to determine DRD2/DRD5 expression broadly across human cancers. Cell viability assays were performed with ONC201 in >1,000 Genomic of Drug Sensitivity in Cancer and NCI60 cell lines. IHC staining of DRD2/DRD5 was performed on tissue microarrays and archival tumor tissues of glioblastoma patients treated with ONC201. Whole exome sequencing was performed in RKO cells with and without acquired ONC201 resistance. Wild-type and mutant DRD5 constructs were generated for overexpression studies. RESULTS: DRD2 overexpression broadly occurs across tumor types and is associated with a poor prognosis. Whole exome sequencing of cancer cells with acquired resistance to ONC201 revealed a de novo Q366R mutation in the DRD5 gene. Expression of Q366R DRD5 was sufficient to induce tumor cell apoptosis, consistent with a gain-of-function. DRD5 overexpression in glioblastoma cells enhanced DRD2/DRD5 heterodimers and DRD5 expression was inversely correlated with innate tumor cell sensitivity to ONC201. Investigation of archival tumor samples from patients with recurrent glioblastoma treated with ONC201 revealed that low DRD5 expression was associated with relatively superior clinical outcomes. CONCLUSIONS: These results implicate DRD5 as a negative regulator of DRD2 signaling and tumor sensitivity to ONC201 DRD2 antagonism.