Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach

2010 ◽  
Vol 50 (5) ◽  
pp. 716-731 ◽  
Author(s):  
Shivani Agarwal ◽  
Deepak Dugar ◽  
Shiladitya Sengupta
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Learning Approach ◽  
Chemical Structures ◽  
Machine Learning Approach ◽  
New Machine

scholarly journals Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Throughput ◽  
In Silico ◽  
Shake Flask ◽  
Chromatographic Method ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
High Throughput Screens

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br>

scholarly journals Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Throughput ◽  
In Silico ◽  
Shake Flask ◽  
Chromatographic Method ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
High Throughput Screens

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br>

scholarly journals Supplementary material to "OPTiMAL: A new machine learning approach for GDGT-based palaeothermometry"

2019 ◽  
Author(s):  
Yvette L. Eley ◽  
William Thompson ◽  
Sarah E. Greene ◽  
Ilya Mandel ◽  
Kirsty Edgar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Supplementary Material ◽  
New Machine

scholarly journals Low Amplitude Burst Detection of Catecholamines

2021 ◽  
Author(s):  
Amnah Eltahir ◽  
Jason White ◽  
Terry Lohrenz ◽  
P. Read Montague
Keyword(s):  
Machine Learning ◽  
Electrochemical Detection ◽  
Perception And Action ◽  
Learning Approach ◽  
Voltage Waveform ◽  
Machine Learning Approach ◽  
Dependent Signal ◽  
Low Amplitude ◽  
New Machine

Abstract Machine learning advances in electrochemical detection have recently produced subsecond and concurrent detection of dopamine and serotonin during perception and action tasks in conscious humans. Here, we present a new machine learning approach to subsecond, concurrent separation of dopamine, norepinephrine, and serotonin. The method exploits a low amplitude burst protocol for the controlled voltage waveform and we demonstrate its efficacy by showing how it separates dopamine-induced signals from norepinephrine induced signals. Previous efforts to deploy electrochemical detection of dopamine in vivo have not separated the dopamine-dependent signal from a norepinephrine-dependent signal. Consequently, this new method can provide new insights into concurrent signaling by these two important neuromodulators.

A New Machine Learning Approach for Protein Phosphorylation Site Prediction in Plants

2009 ◽  
pp. 18-29 ◽  
Author(s):  
Jianjiong Gao ◽  
Ganesh Kumar Agrawal ◽  
Jay J. Thelen ◽  
Zoran Obradovic ◽  
A. Keith Dunker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Phosphorylation ◽  
Phosphorylation Site ◽  
Learning Approach ◽  
Site Prediction ◽  
Machine Learning Approach ◽  
New Machine

A Machine Learning Approach to Identify Specific Small Molecule Inhibitors of Secondary Nucleation in alpha-Synuclein Aggregation

2021 ◽  
Author(s):  
Robert I. Horne ◽  
Andrea Possenti ◽  
Sean Chia ◽  
Z. Faidon Brotzakis ◽  
Roxine Staats ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Chemical Space ◽  
Alpha Synuclein ◽  
Learning Approach ◽  
Secondary Nucleation ◽  
Attrition Rates ◽  
Disease Modifying Drugs ◽  
Docking Simulations ◽  
Machine Learning Approach

Drug development is an increasingly active area of machine learning application, due to the high attrition rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases where very few disease modifying drugs have been approved, demonstrating a need for novel and efficient approaches to drug discovery in this area. However, whether or not machine learning methods can fulfil this role remains to be demonstrated. To explore this possibility, we describe a machine learning approach to identify specific inhibitors of the proliferation of alpha-synuclein aggregates through secondary nucleation, a process that has been implicated in Parkinson's disease and related synucleinopathies. We use a combination of docking simulations followed by machine learning to first identify initial hit compounds and then explore the chemical space around these compounds. Our results demonstrate that this approach leads to the identification of novel chemical matter with an improved hit rate and potency over conventional similarity search approaches.

scholarly journals A new machine learning approach to house price estimation

2018 ◽  
Vol 4 (6) ◽  
pp. 165-171 ◽  
Author(s):  
ChangchunWang Wang ◽  
Hui Wu
Keyword(s):  
Machine Learning ◽  
House Price ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Price Estimation ◽  
New Machine

scholarly journals OPTIMAL: A NEW MACHINE LEARNING APPROACH FOR GDGT-BASED PALAEOTHERMOMETRY

2019 ◽  
Author(s):  
Yvette Eley ◽  
◽  
William Thomson ◽  
Sarah E. Greene ◽  
Ilya Mandel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
New Machine
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