Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells

Kuenzi, Brent M.; Park, Jisoo; Fong, Samson; Sanchez, Kyle S.; Lee, John J.; Kreisberg, Jason F.; Ma, Jianzhu; Ideker, Trey

doi:10.1016/j.ccell.2020.09.014

Cited by 310 publications

(267 citation statements)

References 81 publications

(107 reference statements)

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“…The results shown here are clearly only one more reason for Ab drug development acceleration, in addition to several others, including the increasing role of Ab as individual drugs (Strohl, 2018), the prospect of advances in decoding of intracellular signaling, possibly using methods similar to those used to decipher encrypted messages (Singh, 1999) and developments in the use of artificial intelligence to predict effective drug combinations (Kuenzi et al, 2020).…”

Section: Pharmacological and Medical Implicationsmentioning

confidence: 83%

Naturally occurring combinations of receptors from single cell transcriptomics in endothelial cells

Domanskyi

Hakansson

Meng

et al. 2021

Preprint

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VEGF inhibitor drugs have been successful, especially in ophthalmology, but not all patients respond to them. Combinations of drugs are likely to be needed for a really effective therapy of angiogenesis-related diseases. In this paper we introduce a new concept, the comberon, a term named by analogy with the operon that refers to evolutionarily conserved combinations of co-expressed genes. These genes identify potential drug targets.Our results show that single-cell gene expression data can help to identify a set of co-expressed endothelial cell receptors, conserved among species (mice and human) and enriched, within a network, of connections to up-regulated genes. This set does include VEGF receptors and includes several other receptors previously shown to play a role in angiogenesis. The conclusions are supported by multiple highly significant statistical tests from large datasets, including an independent validation set. This discovery has broad pharmacological and socio-economic implications, going beyond angiogenesis therapy.

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Section: Pharmacological and Medical Implicationsmentioning

confidence: 83%

Naturally occurring combinations of receptors from single cell transcriptomics in endothelial cells

Domanskyi

Hakansson

Meng

et al. 2021

Preprint

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“…Similarly, Kuenzi et al 2020 predicted drug response and synergy using a DL model of human cancer cells. The results concluded that predicted combinations improve progression-free survival, and response predictions stratify ER-positive breast cancer patient clinical outcomes [ 294 ]. Another AI application in drug repurposing comes from the study performed by Wang et al 2020, which used bipartite graph convolutional networks for in silico drug repurposing.…”

Section: Applications Of Artificial Intelligence In Drug Development mentioning

confidence: 99%

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

et al. 2021

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Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

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“…The traditional neural network architecture is the feed forward neural network with one hidden layer, in which each input neuron is connected to each neuron in the hidden layer and which is further connected to each neuron in the output layer. Apart from the subcellular localization prediction [ 13 , 79 ], deep learning is successfully applied in many other biological fields such as the prediction of splicing pattern prediction [ 80 , 81 ], protein secondary structure prediction [ 82 , 83 ], different types of cancer and drug-target interactions [ 84 , 85 , 86 , 87 ], and the patterns in the biomedical imaging datasets [ 88 ].…”

Section: Machine Learning Tools Used In Protein Predictionmentioning

confidence: 99%

Bird Eye View of Protein Subcellular Localization Prediction

Kumar

Dhanda

2020

Life

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Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locations or compartments to perform their structure and functions. The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods. In this review, we reviewed the progress in this field and offered a bird eye view consisting of a comprehensive listing of tools, types of input features explored, machine learning approaches employed, and evaluation matrices applied. We hope the review will be useful for the researchers working in the field of protein localization predictions.

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Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells

Cited by 310 publications

References 81 publications

Naturally occurring combinations of receptors from single cell transcriptomics in endothelial cells

Naturally occurring combinations of receptors from single cell transcriptomics in endothelial cells

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Bird Eye View of Protein Subcellular Localization Prediction

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