The current state of cancer therapeutics has been moving away from one-size-fits-all cytotoxic chemotherapy, and towards a more individualized and specific approach involving the targeting of each tumor's genetic vulnerabilities. Different tumors, even of the same type, may be more reliant on certain cellular pathways more than others. With modern advancements in our understanding of cancer genome sequencing, these pathways can be discovered. Investigating each of the millions of possible small molecule inhibitors for each kinase in vitro, however, would be extremely expensive and time consuming. This project focuses on predicting the inhibition activity of small molecules targeting 8 different kinases using multiple deep learning models. We trained fingerprint-based MLPs and simplified molecular-input line-entry specification (SMILES)-based recurrent neural networks (RNNs) and molecular graph convolutional networks (GCNs) to accurately predict inhibitory activity targeting these 8 kinases.
Novel approaches to oncology drug discovery has been largely spurred by the new understanding of how abnormal protein kinase activity has been linked to the development and onset of a variety of diseases. Different tumors, even of the same type, may be more reliant on certain cellular pathways more than others, which can be discovered through cancer genome sequencing. Previous research has been done in order to characterize the structural basis of kinase inhibitor selectivity, and identify potential kinases that are involved in cancer mechanisms, but there is a lacking of research done to predict potential inhibitors. Once kinases have been identified, the IC-50 values, SMILES codes, connectivity, and other features can be utilized in conjunction with deep learning models to predict the activity of small molecule inhibitors @cite_7 .
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2099787654 2077804275 2320415261 2419673715
The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40 are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled. Inhibition of kinase activity has received enormous interest as a therapeutic strategy for cancer. This Review discusses the current approaches to develop and characterize new inhibitors. Targeted cancer therapies have shown promising results in patients, but few of these drugs provide long-term benefits because tumor cells rapidly develop drug resistance. . show that glioblastoma cells can become resistant to erlotinib, an epidermal growth factor receptor (EGFR)–targeted drug, by eliminating extrachromosomal copies of the mutant EGFR gene. After a period of drug withdrawal, the mutant EGFR gene reappears on extrachromosomal DNA, and the tumor cells become resensitized. The discovery that cancer cells can evade drug therapy by this “hide-and-seek” mechanism may help to optimize the dosing schedule of erlotinib in glioblastoma patients. D. A. Nathanson, B. Gini, J. Mottahedeh, K. Visnyei, T. Koga, G. Gomez, A. Eskin, K. Hwang, J. Wang, K. Masui, A. Paucar, H. Yang, M. Ohashi, S. Zhu, J. Wykosky, R. Reed, S. F. Nelson, T. F. Cloughesy, C. D. James, P. N. Rao, H. I. Kornblum, J. R. Heath, W. K. Cavenee, F. B. Furnari, P. S. Mischel, Targeted therapy resistance mediated by dynamic regulation of extrachromosomal mutant EGFR DNA. Science 343 , 72–76 (2014). [Abstract][Full Text] With the advent of electronic health records, more data are continuously collected for individual patients, and more data are available for review from past patients. Despite this, it has not yet been possible to successfully use this data to systematically build clinical decision support systems that can produce personalized clinical recommendations to assist clinicians in providing individualized healthcare. In this paper, we present a novel approach, discovery engine (DE), that discovers which patient characteristics are most relevant for predicting the correct diagnosis and or recommending the best treatment regimen for each patient. We demonstrate the performance of DE in two clinical settings: diagnosis of breast cancer as well as a personalized recommendation for a specific chemotherapy regimen for breast cancer patients. For each distinct clinical recommendation, different patient features are relevant; DE can discover these different relevant features and use them to recommend personalized clinical decisions. The DE approach achieves a 16.6 improvement over existing state-of-the-art recommendation algorithms regarding kappa coefficients for recommending the personalized chemotherapy regimens. For diagnostic predictions, the DE approach achieves a 2.18 and 4.20 improvement over existing state-of-the-art prediction algorithms regarding prediction error rate and false positive rate, respectively. We also demonstrate that the performance of our approach is robust against missing information and that the relevant features discovered by DE are confirmed by clinical references.