Machine Learning and Perturbation Theory Machine Learning (PTML) in Medicinal Chemistry, Biotechnology, and Nanotechnology

2021 ◽  
Vol 21 ◽  
Author(s):  
Diana M. Herrera-Ibatá
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
Drug Discovery ◽  
Medicinal Chemistry ◽  
Molecular Descriptors ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Drug Candidates ◽  
Protein Research ◽  
New Compounds

: Recently different authors have reported Perturbation Theory (PT) methods combined with machine learning (ML) to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems. Here we present one state-of-art review about the different applications of PTML models in Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. The aim of the models is to find relations between the molecular descriptors and the biological characteristics to predict key properties of new compounds. An area where the ML has been very useful is the drug discovery process. The entire process of drug discovery leads to the generation of lots of data, and it is also a costly and time-consuming process. ML comes with the opportunity of analyzing great amounts of chemical data obtaining outcomes to find potential drug candidates.

scholarly journals Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening

2020 ◽  
Author(s):  
Pedro Ballester
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Predictive Accuracy ◽  
Scoring Function ◽  
3D Models ◽  
Large Datasets ◽  
Scoring Functions ◽  
Discovery Process ◽  
Drug Discovery Process

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

scholarly journals Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening

2020 ◽  
Author(s):  
Pedro Ballester
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Predictive Accuracy ◽  
Scoring Function ◽  
3D Models ◽  
Large Datasets ◽  
Scoring Functions ◽  
Discovery Process ◽  
Drug Discovery Process

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

scholarly journals Protein X-ray Crystallography and Drug Discovery

Molecules ◽  
2020 ◽  
Vol 25 (5) ◽  
pp. 1030 ◽  
Author(s):  
Laurent Maveyraud ◽  
Lionel Mourey
Keyword(s):  
Drug Discovery ◽  
Structural Information ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
X Ray ◽  
Drug Candidates ◽  
X Ray Crystallography ◽  
Compound Libraries ◽  
Fragment Library ◽  
Invaluable Tool

With the advent of structural biology in the drug discovery process, medicinal chemists gained the opportunity to use detailed structural information in order to progress screening hits into leads or drug candidates. X-ray crystallography has proven to be an invaluable tool in this respect, as it is able to provide exquisitely comprehensive structural information about the interaction of a ligand with a pharmacological target. As fragment-based drug discovery emerged in the recent years, X-ray crystallography has also become a powerful screening technology, able to provide structural information on complexes involving low-molecular weight compounds, despite weak binding affinities. Given the low numbers of compounds needed in a fragment library, compared to the hundreds of thousand usually present in drug-like compound libraries, it now becomes feasible to screen a whole fragment library using X-ray crystallography, providing a wealth of structural details that will fuel the fragment to drug process. Here, we review theoretical and practical aspects as well as the pros and cons of using X-ray crystallography in the drug discovery process.

scholarly journals Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3139
Author(s):  
Fazileh Esmaeili ◽  
Tahmineh Lohrasebi ◽  
Manijeh Mohammadi-Dehcheshmeh ◽  
Esmaeil Ebrahimie
Keyword(s):  
Machine Learning ◽  
Transcription Factors ◽  
Drug Discovery ◽  
Cancer Cells ◽  
Meta Analysis ◽  
Herbal Extract ◽  
Integrative Approach ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Herbal Components

Predicting cancer cells’ response to a plant-derived agent is critical for the drug discovery process. Recently transcriptomes advancements have provided an opportunity to identify regulatory signatures to predict drug activity. Here in this study, a combination of meta-analysis and machine learning models have been used to determine regulatory signatures focusing on differentially expressed transcription factors (TFs) of herbal components on cancer cells. In order to increase the size of the dataset, six datasets were combined in a meta-analysis from studies that had evaluated the gene expression in cancer cell lines before and after herbal extract treatments. Then, categorical feature analysis based on the machine learning methods was applied to examine transcription factors in order to find the best signature/pattern capable of discriminating between control and treated groups. It was found that this integrative approach could recognize the combination of TFs as predictive biomarkers. It was observed that the random forest (RF) model produced the best combination rules, including AIP/TFE3/VGLL4/ID1 and AIP/ZNF7/DXO with the highest modulating capacity. As the RF algorithm combines the output of many trees to set up an ultimate model, its predictive rules are more accurate and reproducible than other trees. The discovered regulatory signature suggests an effective procedure to figure out the efficacy of investigational herbal compounds on particular cells in the drug discovery process.

scholarly journals Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Zhang ◽  
Taoyu Ye ◽  
Hui Xi ◽  
Mario Juhas ◽  
Junyi Li
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Severe Acute Respiratory Syndrome ◽  
Infectious Diseases ◽  
Broad Spectrum ◽  
Antimicrobial Compounds ◽  
Discovery Process ◽  
Future Perspectives ◽  
Drug Discovery Process ◽  
Drug Candidates

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

Perturbation Theory Machine Learning Models: Theory, Regulatory Issues, and Applications to Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology

2018 ◽  
Vol 18 (14) ◽  
pp. 1203-1213 ◽  
Author(s):  
Sonia Arrasate ◽  
Aliuska Duardo-Sanchez
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
Organic Synthesis ◽  
Medicinal Chemistry ◽  
Main Question ◽  
Small Organic Molecules ◽  
Regulatory Issues ◽  
Protein Research ◽  
The Cost

Machine Learning (ML) models are very useful to predict physicochemical properties of small organic molecules, proteins, proteomes, and complex systems. These methods may be useful to reduce the cost of research in terms of materials resources, time, and laboratory animal sacrifice. Recently different authors have reported Perturbation Theory (PT) methods combined with ML to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems and in technology as well. Here, we present one state-of- the-art review about the different applications of PTML models in Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. In this work, we also embrace an overview of regulatory issues for acceptance and validation of both: the Cheminformatics models, and the characterization of new Biomaterials. This is a main question in order to make scientific result self for humans and environment.

Sign in / Sign up

Export Citation Format

Share Document