In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling

2020 ◽  
Vol 8 (1) ◽  
pp. 107-123 ◽  
Author(s):  
Shivam Saxena ◽  
Tuhin Suvra Khan ◽  
Fatima Jalid ◽  
Manojkumar Ramteke ◽  
M. Ali Haider
Keyword(s):  
Machine Learning ◽  
Ab Initio ◽  
High Throughput ◽  
Material Properties ◽  
In Silico ◽  
High Throughput Screening ◽  
Chemical Engineering ◽  
Materials Science ◽  
Single Atom ◽  
Single Atom Alloys

The advent of machine learning (ML) techniques in solving problems related to materials science and chemical engineering is driving expectations to give faster predictions of material properties.

Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts

2020 ◽  
Vol 8 (10) ◽  
pp. 5209-5216 ◽  
Author(s):  
Mohammad Zafari ◽  
Deepak Kumar ◽  
Muhammad Umer ◽  
Kwang S. Kim
Keyword(s):  
Machine Learning ◽  
Nitrogen Fixation ◽  
High Throughput ◽  
High Throughput Screening ◽  
Reduction Reaction ◽  
Single Atom ◽  
Nitrogen Reduction ◽  
Computational Burden ◽  
Boron Doped

Machine learning (ML) methods would significantly reduce the computational burden of catalysts screening for nitrogen reduction reaction (NRR).

scholarly journals Accelerating organic solar cell material's discovery: high-throughput screening and big data

2021 ◽  
Author(s):  
Xabier Rodríguez-Martínez ◽  
Enrique Pascual-San-José ◽  
Mariano Campoy-Quiles
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Throughput ◽  
Organic Solar Cells ◽  
High Throughput Screening ◽  
Organic Solar Cell ◽  
State Of The Art ◽  
Review Article ◽  
Machine Learning Algorithms ◽  
Device Optimization

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms.

scholarly journals A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

2019 ◽  
Vol 10 (36) ◽  
pp. 8374-8383 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Aditya Sonpal ◽  
Mojtaba Haghighatlari ◽  
Andrew J. Schultz ◽  
Johannes Hachmann
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Refractive Index ◽  
High Throughput ◽  
Neural Network Model ◽  
High Throughput Screening ◽  
Deep Neural Network ◽  
Organic Molecules ◽  
High Refractive Index ◽  
Computational Pipeline

Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.

Discovery of indenopyrazoles as EGFR and VEGFR-2 tyrosine kinase inhibitors by in silico high-throughput screening

2008 ◽  
Vol 18 (1) ◽  
pp. 285-288 ◽  
Author(s):  
Taikou Usui ◽  
Hyun Seung Ban ◽  
Junpei Kawada ◽  
Takatsugu Hirokawa ◽  
Hiroyuki Nakamura
Keyword(s):  
Tyrosine Kinase ◽  
Tyrosine Kinase Inhibitors ◽  
High Throughput ◽  
In Silico ◽  
High Throughput Screening ◽  
Kinase Inhibitors

scholarly journals Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

2021 ◽  
Author(s):  
Jeremy Feinstein ◽  
ganesh sivaraman ◽  
Kurt Picel ◽  
Brian Peters ◽  
Alvaro Vazquez-Mayagoitia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
High Throughput ◽  
High Throughput Screening ◽  
Organic Chemical ◽  
Learning Method ◽  
Toxicity Data ◽  
Chemical Structures ◽  
Machine Learning Methods ◽  
Computational Methodology

In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed.

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 Application of Life Cycle Assessment and Machine Learning for High-Throughput Screening of Green Chemical Substitutes

2020 ◽  
Author(s):  
Xinzhe Zhu ◽  
Chi-Hung Ho ◽  
Xiaonan Wang
Keyword(s):  
Machine Learning ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Production Process ◽  
Environmental Impacts ◽  
High Throughput ◽  
High Throughput Screening ◽  
Molecular Descriptors ◽  
Trifluoroacetic Anhydride ◽  
Chemical Materials

<p><a></a><a>The production process of many active pharmaceutical ingredients such as sitagliptin could cause severe environmental problems due to the use of toxic chemical materials and production infrastructure, energy consumption and wastes treatment. The environmental impacts of sitagliptin production process were estimated with life cycle assessment (LCA) method, which suggested that the use of chemical materials provided the major environmental impacts. Both methods of Eco-indicator 99 and ReCiPe endpoints confirmed that chemical feedstock accounted 83% and 70% of life-cycle impact, respectively. Among all the chemical materials used in the sitagliptin production process, </a><a>trifluoroacetic anhydride </a>was identified as the largest influential factor in most impact categories according to the results of ReCiPe midpoints method. Therefore, high-throughput screening was performed to seek for green chemical substitutes to replace the target chemical (i.e. trifluoroacetic anhydride) by the following three steps. Firstly, thirty most similar chemicals were obtained from two million candidate alternatives in PubChem database based on their molecular descriptors. Thereafter, deep learning neural network models were developed to predict life-cycle impact according to the chemicals in Ecoinvent v3.5 database with known LCA values and corresponding molecular descriptors. Finally, 1,2-ethanediyl ester was proved to be one of the potential greener substitutes after the LCA data of these similar chemicals were predicted using the well-trained machine learning models. The case study demonstrated the applicability of the novel framework to screen green chemical substitutes and optimize the pharmaceutical manufacturing process.</p>

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