scholarly journals CASTELO: Clustered Atom Subtypes Aided Lead Optimization - A Combined Machine Learning and Molecular Modeling Method

2021 ◽  
Author(s):  
Leili Zhang ◽  
Giacomo Domeniconi ◽  
Chih-Chieh Yang ◽  
Seung-gu Kang ◽  
Ruhong Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Modeling ◽  
Lead Optimization ◽  
Clustering Methods ◽  
Activity Data ◽  
Multi Stage ◽  
Drug Potency ◽  
Variational Autoencoder ◽  
Stage Process ◽  
Drug Modification

Abstract Background: Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots.Results: The initial data collection is achieved with physics-based molecular dynamics (MD) simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization.Conclusion: With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.

scholarly journals CASTELO: clustered atom subtypes aided lead optimization—a combined machine learning and molecular modeling method

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Leili Zhang ◽  
Giacomo Domeniconi ◽  
Chih-Chieh Yang ◽  
Seung-gu Kang ◽  
Ruhong Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Modeling ◽  
Lead Optimization ◽  
Dynamics Simulation ◽  
Clustering Methods ◽  
Activity Data ◽  
Multi Stage ◽  
Drug Potency ◽  
Variational Autoencoder ◽  
Drug Modification

Abstract Background Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots. Results The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization. Conclusion With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.

A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

2021 ◽  
Vol 40 (5) ◽  
pp. 9471-9484
Author(s):  
Yilun Jin ◽  
Yanan Liu ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Yu Lou
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Imbalanced Data ◽  
Ensemble Model ◽  
Selective Sampling ◽  
Machine Learning Methods ◽  
Multi Stage ◽  
Proposed Model ◽  
New Feature

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

Toward Reducing hERG Affinities for DAT Inhibitors with a Combined Machine Learning and Molecular Modeling Approach

2021 ◽  
Vol 61 (9) ◽  
pp. 4266-4279 ◽  
Author(s):  
Kuo Hao Lee ◽  
Andrew D. Fant ◽  
Jiqing Guo ◽  
Andy Guan ◽  
Joslyn Jung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Modeling ◽  
Modeling Approach

scholarly journals Detecting Cognitive Impairment Status Using Keystroke Patterns and Physical Activity Data among the Older Adults: A Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mohammad Nahid Hossain ◽  
Mohammad Helal Uddin ◽  
K. Thapa ◽  
Md Abdullah Al Zubaer ◽  
Md Shafiqul Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Cognitive Impairment ◽  
Learning Algorithm ◽  
Negative Impact ◽  
Gradient Boosting ◽  
Feature Selection Technique ◽  
Activity Data ◽  
Physical Data ◽  
Machine Learning Model

Cognitive impairment has a significantly negative impact on global healthcare and the community. Holding a person’s cognition and mental retention among older adults is improbable with aging. Early detection of cognitive impairment will decline the most significant impact of extended disease to permanent mental damage. This paper aims to develop a machine learning model to detect and differentiate cognitive impairment categories like severe, moderate, mild, and normal by analyzing neurophysical and physical data. Keystroke and smartwatch have been used to extract individuals’ neurophysical and physical data, respectively. An advanced ensemble learning algorithm named Gradient Boosting Machine (GBM) is proposed to classify the cognitive severity level (absence, mild, moderate, and severe) based on the Standardised Mini-Mental State Examination (SMMSE) questionnaire scores. The statistical method “Pearson’s correlation” and the wrapper feature selection technique have been used to analyze and select the best features. Then, we have conducted our proposed algorithm GBM on those features. And the result has shown an accuracy of more than 94%. This paper has added a new dimension to the state-of-the-art to predict cognitive impairment by implementing neurophysical data and physical data together.

scholarly journals Insights into the Cellular and Molecular Mechanisms That Govern the Fracture-Healing Process: A Narrative Review

2021 ◽  
Vol 10 (16) ◽  
pp. 3554
Author(s):  
Dionysios J. Papachristou ◽  
Stavros Georgopoulos ◽  
Peter V. Giannoudis ◽  
Elias Panagiotopoulos
Keyword(s):  
Fracture Healing ◽  
Bone Repair ◽  
Molecular Mechanisms ◽  
Current Knowledge ◽  
Healing Process ◽  
Bone Architecture ◽  
Multi Stage ◽  
Stage Process ◽  
And Function ◽  
The Impact

Fracture-healing is a complex multi-stage process that usually progresses flawlessly, resulting in restoration of bone architecture and function. Regrettably, however, a considerable number of fractures fail to heal, resulting in delayed unions or non-unions. This may significantly impact several aspects of a patient’s life. Not surprisingly, in the past few years, a substantial amount of research and number of clinical studies have been designed, aiming at shedding light into the cellular and molecular mechanisms that regulate fracture-healing. Herein, we present the current knowledge on the pathobiology of the fracture-healing process. In addition, the role of skeletal cells and the impact of marrow adipose tissue on bone repair is discussed. Unveiling the pathogenetic mechanisms that govern the fracture-healing process may lead to the development of novel, smarter, and more effective therapeutic strategies for the treatment of fractures, especially of those with large bone defects.

scholarly journals Machine Learning in a Molecular Modeling Course for Chemistry, Biochemistry, and Biophysics Students

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Jacob M. Remington ◽  
Jonathon B. Ferrell ◽  
Marlo Zorman ◽  
Adam Petrucci ◽  
Severin T. Schneebeli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Modeling ◽  
Student Learning ◽  
Molecular Properties ◽  
Computer Hardware ◽  
Data Driven ◽  
Teaching Approaches ◽  
Advanced Level ◽  
Interdisciplinary Field ◽  
Elective Course

ABSTRACT Recent advances in computer hardware and software, particularly the availability of machine learning (ML) libraries, allow the introduction of data-based topics such as ML into the biophysical curriculum for undergraduate and graduate levels. However, there are many practical challenges of teaching ML to advanced level students in biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogic tools, and assessments of student learning, to develop the new methodology to incorporate the basis of ML in an existing biophysical elective course and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply ML algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using ML algorithms. This work establishes a framework for future teaching approaches that unite ML and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.

scholarly journals Classification of Platinum Nanoparticle Catalysts using Machine Learning

2020 ◽  
Author(s):  
Amanda J. Parker ◽  
George Opletal ◽  
Amanda Barnard
Keyword(s):  
Machine Learning ◽  
Hydrogen Oxidation ◽  
Pt Nanoparticles ◽  
Structure Property ◽  
Metallic Nanoparticle ◽  
Platinum Nanoparticle ◽  
Nanoparticle Catalysts ◽  
Structure Property Relationships ◽  
Multi Stage

Computer simulations and machine learning provide complementary ways of identifying structure/property relationships that are typically targeting toward predicting the ideal singular structure to maximise the performance on a given application. This can be inconsistent with experimental observations that measure the collective properties of entire samples of structures that contain distributions or mixture of structures, even when synthesized and processed with care. Metallic nanoparticle catalysts are an important example. In this study we have used a multi-stage machine learning workflow to identify the correct structure/property relationships of Pt nanoparticles relevant to oxygen reduction (ORR), hydrogen oxidation (HOR) and hydrogen evolution (HER) reactions. By including classification prior to regression we identified two distinct classes of nanoparticles, and subsequently generate the class-specific models based on experimentally relevant criteria that are consistent with observations. These multi-structure/multi-property relationships, predicting properties averaged over a large sample of structures, provide a more accessible way to transfer data-driven predictions into the lab.

scholarly journals Epigenetic and transcriptional regulation of osteoclastogenesis in the pathogenesis of skeletal diseases: a protocol for a systematic review

2020 ◽  
Author(s):  
Dr Siddhartha Sharma ◽  
Dr Anupam Mittal ◽  
Aditi Mahajan ◽  
Riddhi Gohil
Keyword(s):  
Rheumatoid Arthritis ◽  
Systematic Review ◽  
Genetic Material ◽  
Transcriptional Regulators ◽  
Osteoclast Formation ◽  
Micro Rna ◽  
Epigenetic Changes ◽  
Multi Stage ◽  
Stage Process ◽  
Skeletal Disorders

Osteoclastogenesis (OCG) is a multi-stage process that involves formation of activated osteoclasts from bone marrow macrophages. The progression of each stage of OCG is governed by a set of transcription factors and gene regulators, which are genetically and epigenetically regulated at both transcription and post-transcription levels. Epigenetic changes are used to denote interactions between genetic material and environment leading to phenotypic alterations that can be inherited, without any variations in DNA sequence. Epigenetic and transcription regulatory events have profound effects on osteoclast formation and activation; these have been implicated in numerous disorders including osteoporosis, osteopetrosis, rheumatoid arthritis, spondyloarthritis, gout and bone metastasis. We aim to conduct a systematic review to assess possible relationship between key epigenetic and transcriptional regulators of osteoclastogenesis, and their role in specific bone disorders. This is a protocol for the proposed review. Keywords: osteoclasts; osteoclastogenesis; epigenetics; transcription; bone; skeletal disorders; methylation; histone proteins; micro RNA

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