scholarly journals Kinome-wide activity classification of small molecules by deep learning

2019 ◽  
Author(s):  
Bryce K Allen ◽  
Nagi G Ayad ◽  
Stephan C Schürer
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Small Molecules ◽  
Small Molecule ◽  
Kinase Inhibitors ◽  
Structural Features ◽  
Linear Transformations ◽  
Resistant Disease ◽  
Small Molecule Inhibition ◽  
High Level

Deep learning is a machine learning technique that attempts to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing structural features of small molecules to predict activity. However, the application of deep learning to discriminating features of kinase inhibitors has not been well explored. Small molecule kinase inhibitors are an important class of anti-cancer agents and have demonstrated impressive clinical efficacy in several different diseases. However, resistance is often observed mediated by adaptive Kinome reprogramming or subpopulation diversity. Therefore, polypharmacology and combination therapies offer potential therapeutic strategies for patients with resistant disease. Their development would benefit from more comprehensive and dense knowledge of small-molecule inhibition across the human Kinome. Because such data is not publicly available, we evaluated multiple machine learning methods to predict small molecule inhibition of 342 kinases using over 650K aggregated bioactivity annotations for over 300K small molecules curated from ChEMBL and the Kinase Knowledge Base (KKB). Our results demonstrated that multi-task deep neural networks outperform classical single-task methods, offering potential towards predicting activity profiles and filling gaps in the available data.

Deep Learning Network

2020 ◽  
pp. 1-30
Author(s):  
Bhanu Chander
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Great Power ◽  
Human Being ◽  
Technology Improvement ◽  
Deep Learning Network ◽  
High Level ◽  
Human Brains ◽  
Near Future

Artificial intelligence (AI) is defined as a machine that can do everything a human being can do and produce better results. Means AI enlightening that data can produce a solution for its own results. Inside the AI ellipsoidal, Machine learning (ML) has a wide variety of algorithms produce more accurate results. As a result of technology, improvement increasing amounts of data are available. But with ML and AI, it is very difficult to extract such high-level, abstract features from raw data, moreover hard to know what feature should be extracted. Finally, we now have deep learning; these algorithms are modeled based on how human brains process the data. Deep learning is a particular kind of machine learning that provides flexibility and great power, with its attempts to learn in multiple levels of representation with the operations of multiple layers. Deep learning brief overview, platforms, Models, Autoencoders, CNN, RNN, and Appliances are described appropriately. Deep learning will have many more successes in the near future because it requires very little engineering by hand.

In depth analysis of kinase cross screening data to identify chemical starting points for inhibition of the nek family of kinases

2017 ◽  
Author(s):  
Carrow I. Wells ◽  
Nirav R. Kapadia ◽  
Rafael M. Couñago ◽  
David H. Drewry
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Small Molecule ◽  
Kinase Inhibitors ◽  
Biological Functions ◽  
Chemical Probes ◽  
High Quality ◽  
Depth Analysis ◽  
Number Of Citations ◽  
Shed Light

AbstractPotent, selective, and cell active small molecule kinase inhibitors are useful tools to help unravel the complexities of kinase signaling. As the biological functions of individual kinases become better understood, they can become targets of drug discovery efforts. The small molecules used to shed light on function can also then serve as chemical starting points in these drug discovery efforts. The Nek family of kinases has received very little attention, as judged by number of citations in PubMed, yet they appear to play many key roles and have been implicated in disease. Here we present our work to identify high quality chemical starting points that have emerged due to the increased incidence of broad kinome screening. We anticipate that this analysis will allow the community to progress towards the generation of chemical probes and eventually drugs that target members of the Nek family.

scholarly journals Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review

Diagnostics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 29 ◽  
Author(s):  
Lea Pehrson ◽  
Michael Nielsen ◽  
Carsten Ammitzbøl Lauridsen
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Ct Scans ◽  
Lung Nodules ◽  
Original Research ◽  
Feature Based ◽  
High Level ◽  
Meta Analyses

The aim of this study was to provide an overview of the literature available on machine learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection (LIDC-IDRI) database as a tool for the optimization of detecting lung nodules in thoracic CT scans. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning algorithms applied to the LIDC-IDRI database were included. The initial search yielded 1972 publications after removing duplicates, and 41 of these articles were included in this study. The articles were divided into two subcategories describing their overall architecture. The majority of feature-based algorithms achieved an accuracy >90% compared to the deep learning (DL) algorithms that achieved an accuracy in the range of 82.2%–97.6%. In conclusion, ML and DL algorithms are able to detect lung nodules with a high level of accuracy, sensitivity, and specificity using ML, when applied to an annotated archive of CT scans of the lung. However, there is no consensus on the method applied to determine the efficiency of ML algorithms.

Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

First Monday ◽  
2019 ◽  
Author(s):  
Niel Chah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Historical Context ◽  
Historical Background ◽  
Deep Roots ◽  
Technical Guide ◽  
Learning Machine ◽  
High Level

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.

scholarly journals Computational Design of Targeted Inhibitors of Polo-Like Kinase 1 (Plk1)

2012 ◽  
Vol 6 ◽  
pp. BBI.S8971 ◽  
Author(s):  
Krupa S. Jani ◽  
D.S. Dalafave
Keyword(s):  
Small Molecules ◽  
Kinase Inhibitors ◽  
Computational Design ◽  
Structural Features ◽  
Docking Studies ◽  
Unique Region ◽  
Atp Binding Site ◽  
Optimal Drug ◽  
Targeted Inhibitors ◽  
Specific Inhibitors

Computational design of small molecule putative inhibitors of Polo-like kinase 1 (Plk1) is presented. Plk1, which regulates the cell cycle, is often over expressed in cancers. Down regulation of Plk1 has been shown to inhibit tumor progression. Most kinase inhibitors interact with the ATP binding site on Plk1, which is highly conserved. This makes the development of Plk1-specific inhibitors challenging, since different kinases have similar ATP sites. However, Plk1 also contains a unique region called the polo-box domain (PBD), which is absent from other kinases. In this study, the PBD site was used as a target for designed Plk1 putative inhibitors. Common structural features of several experimentally known Plk1 ligands were first identified. The findings were used to design small molecules that specifically bonded Plk1. Drug likeness and possible toxicities of the molecules were investigated. Molecules with no implied toxicities and optimal drug likeness values were used for docking studies. Several molecules were identified that made stable complexes only with Plk1 and LYN kinases, but not with other kinases. One molecule was found to bind exclusively the PBD site of Plk1. Possible utilization of the designed molecules in drugs against cancers with over expressed Plk1 is discussed.

scholarly journals Deep Learning to Detect Skin Cancer using Google Colab

2019 ◽  
Vol 8 (6) ◽  
pp. 2176-2183 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Skin Cancer ◽  
Domain Knowledge ◽  
Error Rates ◽  
Data Set ◽  
Training Models ◽  
Learning Machine ◽  
High Level

Different mathematical models, Artificial Intelligence approach and Past recorded data set is combined to formulate Machine Learning. Machine Learning uses different learning algorithms for different types of data and has been classified into three types. The advantage of this learning is that it uses Artificial Neural Network and based on the error rates, it adjusts the weights to improve itself in further epochs. But, Machine Learning works well only when the features are defined accurately. Deciding which feature to select needs good domain knowledge which makes Machine Learning developer dependable. The lack of domain knowledge affects the performance. This dependency inspired the invention of Deep Learning. Deep Learning can detect features through self-training models and is able to give better results compared to using Artificial Intelligence or Machine Learning. It uses different functions like ReLU, Gradient Descend and Optimizers, which makes it the best thing available so far. To efficiently apply such optimizers, one should have the knowledge of mathematical computations and convolutions running behind the layers. It also uses different pooling layers to get the features. But these Modern Approaches need high level of computation which requires CPU and GPUs. In case, if, such high computational power, if hardware is not available then one can use Google Colaboratory framework. The Deep Learning Approach is proven to improve the skin cancer detection as demonstrated in this paper. The paper also aims to provide the circumstantial knowledge to the reader of various practices mentioned above.

scholarly journals Physicochemical and Structural Parameters Contributing to the Antibacterial Activity and Efflux Susceptibility of Small Molecule Inhibitors of Escherichia coli

2021 ◽  
Author(s):  
Sara S. El Zahed ◽  
Shawn French ◽  
Maya A. Farha ◽  
Garima Kumar ◽  
Eric D. Brown
Keyword(s):  
Machine Learning ◽  
Escherichia Coli ◽  
Antibacterial Activity ◽  
Small Molecules ◽  
Small Molecule ◽  
In Silico ◽  
Molecular Descriptors ◽  
Structural Parameters ◽  
Side Chain ◽  
Gram Negative

Discovering new Gram-negative antibiotics has been a challenge for decades. This has been largely attributed to a limited understanding of the molecular descriptors governing Gram-negative permeation and efflux evasion. Herein, we address the contribution of efflux using a novel approach that applies multivariate analysis, machine learning, and structure-based clustering to some 4,500 actives from a small molecule screen in efflux-compromised Escherichia coli. We employed principal-component analysis and trained two decision tree-based machine learning models to investigate descriptors contributing to the antibacterial activity and efflux susceptibility of these actives. This approach revealed that the Gram-negative activity of hydrophobic and planar small molecules with low molecular stability is limited to efflux-compromised E. coli. Further, molecules with reduced branching and compactness showed increased susceptibility to efflux. Given these distinct properties that govern efflux, we developed the first machine learning model, called Susceptibility to Efflux Random Forest (SERF), as a tool to analyze the molecular descriptors of small molecules and predict those that could be susceptible to efflux pumps in silico. Here, SERF demonstrated high accuracy in identifying such molecules. Further, we clustered all 4,500 actives based on their core structures and identified distinct clusters highlighting side chain moieties that cause marked changes in efflux susceptibility. In all, our work reveals a role for physicochemical and structural parameters in governing efflux, presents a machine learning tool for rapid in silico analysis of efflux susceptibility, and provides a proof of principle for the potential of exploiting side chain modification to design novel antimicrobials evading efflux pumps.

scholarly journals Investigating the NRAS 5' UTR as a Target for Small Molecules

2022 ◽  
Author(s):  
Sumirtha Balaratnam ◽  
Zachary R Torrey ◽  
David R. Calabrese ◽  
Michael T Banco ◽  
Kamyar Yazdani ◽  
...  
Keyword(s):  
Small Molecules ◽  
Cell Lines ◽  
Small Molecule ◽  
Pcr Analysis ◽  
Sequencing Data ◽  
Small Molecule Inhibition ◽  
Ras Family ◽  
Control Translation ◽  
Rapid Amplification

Neuroblastoma RAS (NRAS) is an oncogene that is deregulated and highly mutated in cancers including melanomas and acute myeloid leukemias. Constitutively activated NRAS induces the MAPK and AKT signaling pathways and leads to uncontrolled proliferation and cell growth, making it an attractive target for small molecule inhibition. Like all RAS-family proteins, it has proven difficult to identify small molecules that directly inhibit the protein. An alternative approach would involve targeting the NRAS mRNA. The 5′ untranslated region (5′ UTR) of the NRAS mRNA is reported to contain a G-quadruplex (G4) that regulates translation of NRAS mRNA. Stabilizing the G4 structure with small molecules could reduce NRAS protein expression in cancer cells by impacting translation. Here we report a novel class of small molecule that binds to the G4 structure located in the 5′ UTR of the NRAS mRNA. We used a small molecule microarray (SMM) screen to identify molecules that selectively bind to the NRAS-G4. Biophysical studies demonstrated that compound 18 binds reversibly to the NRAS-G4 structure with submicromolar affinity. A Luciferase based reporter assay indicated that 18 inhibits the translation of NRAS via stabilizing the NRAS-G4 in vitro but showed only moderate effects on the NRAS levels in cellulo. Rapid Amplification of cDNA Ends (RACE), RT-PCR analysis on 14 different NRAS-expressing cell lines, coupled with analysis of publicly available CAGE seq experiments, revealed that predominant NRAS transcript does not possess the G4 structure. Further analysis of published rG4 and G4 sequencing data indicated the presence of G4 structure in the promoter region of NRAS gene (DNA) but not in the mRNA. Thus, although many NRAS transcripts lack a G4 in many cell lines the broader concept of targeting folded regions within 5' UTRs to control translation remains a highly attractive strategy and this work represents an intriguing example of transcript heterogeneity impacting targetability.

Deep Learning

2016 ◽  
Vol 10 (03) ◽  
pp. 417-439 ◽  
Author(s):  
Xing Hao ◽  
Guigang Zhang ◽  
Shang Ma
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Complex Structures ◽  
Training Algorithms ◽  
High Level ◽  
And Training

Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and the power of computation, neural networks with more complex structures have attracted widespread attention and been applied to various fields. This paper provides an overview of deep learning in neural networks including popular architecture models and training algorithms.

ChemInformatics Model Explorer (CIME): Exploratory analysis of chemical model explanations

2021 ◽  
Author(s):  
Christina Humer ◽  
Henry Heberle ◽  
Floriane Montanari ◽  
Thomas Wolf ◽  
Florian Huber ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Small Molecule ◽  
Daily Basis ◽  
Data Sets ◽  
Visualization Tool ◽  
Web Based ◽  
Pharmacokinetic Properties ◽  
Opening Up ◽  
Web Based System

The introduction of machine learning to small molecule research – an inherently multidisciplinary field in which chemists and data scientists combine their expertise and collaborate – has been vital to making screening processes more efficient. In recent years, numerous models that predict pharmacokinetic properties or bioactivity have been published, and these are used on a daily basis by chemists to make decisions and prioritize ideas. The emerging field of explainable artificial intelligence is opening up new possibilities for understanding the reasoning that underlies a model. In small molecule research, this means relating contributions of substructures of compounds to their predicted properties, which in turn also allows the areas of the compounds that have the greatest influence on the outcome to be identified. However, there is no interactive visualization tool that facilitates such interdisciplinary collaborations towards interpretability of machine learning models for small molecules. To fill this gap, we present CIME (ChemInformatics Model Explorer), an interactive web-based system that allows users to inspect chemical data sets, visualize model explanations, compare interpretability techniques, and explore subgroups of compounds. The tool is model-agnostic and can be run on a server or a workstation.

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