Model-based Target Pharmacology Assessment (mTPA): An Approach Using PBPK/PD Modeling and Machine Learning to Design Medicinal Chemistry and DMPK Strategies in Early Drug Discovery

2021 ◽  
Vol 64 (6) ◽  
pp. 3185-3196
Author(s):  
Emile P. Chen ◽  
Robert W. Bondi ◽  
Paul J. Michalski
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Medicinal Chemistry ◽  
Model Based ◽  
Early Drug

Improving machine learning in early drug discovery

2017 ◽  
Vol 81 (1-2) ◽  
pp. 155-166 ◽  
Author(s):  
Claus Bendtsen ◽  
Andrea Degasperi ◽  
Ernst Ahlberg ◽  
Lars Carlsson
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Early Drug

scholarly journals An Ultrafast and Flexible LC-MS/MS System Paves the Way for Machine Learning Driven Sample Processing and Data Evaluation in Early Drug Discovery

2021 ◽  
Author(s):  
Tim Häbe ◽  
Christian Späth ◽  
Steffen Schrade ◽  
Wolfgang Jörg ◽  
Roderich Süssmuth ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Drug Discovery ◽  
Data Evaluation ◽  
In Vivo Studies ◽  
Integrated Analysis ◽  
Limit Of Quantitation ◽  
Early Drug ◽  
The Way

Rationale: Low speed and flexibility of most LC-MS/MS approaches in early drug discovery delays sample analysis from routine in vivo studies within the same day of measurements. A highthroughput platform for the rapid quantification of drug compounds in various in vivo assays was developed and established in routine bioanalysis. Methods: Automated selection of an efficient and adequate LC method was realized by autonomous sample qualification for ultrafast batch gradients (9 s/sample) or for fast linear gradients (45 s/sample) if samples require chromatography. The hardware and software components of our Rapid and Integrated Analysis System (RIAS) were streamlined for increased analytical throughput via state-of-the-art automation while keeping high analytical quality. Results: Online decision-making was based on a quick assay suitability test (AST) based on a small and dedicated sample set evaluated by two different strategies. 84% of the acquired data points were within ±30% accuracy and 93% of the deviations between the lower limit of quantitation (LLOQ) values were ≤2-fold compared to standard LC-MS/MS systems while speed, flexibility and overall automation was significantly improved. Conclusions: The developed platform provided an analysis time of only 10 min (batch-mode) and 50 min (gradient-mode) per standard pharmacokinetic (PK) study (62 injections). Automation, data evaluation and results handling were optimized to pave the way for machine learning based decision-making regarding the evaluation strategy of the AST

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 Machine Learning for Molecular Modelling in Drug Design

Biomolecules ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 216 ◽  
Author(s):  
Pedro J. Ballester
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
Molecular Modelling ◽  
Crucial Component ◽  
Early Drug

Machine learning (ML) has become a crucial component of early drug discovery [...]

scholarly journals Artificial Intelligence and Machine Learning in Medicinal Chemistry and Validation of Emerging Drug Targets

2021 ◽  
Author(s):  
Sameer Quazi ◽  
Rohit Jangi
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Drug Targets ◽  
Medicinal Chemistry ◽  
De Novo ◽  
Drug Molecules ◽  
Learning Programs ◽  
Artificial Learning ◽  
Pharmaceutical Industries ◽  
Short Time

Artificial learning and machine learning is playing a pivotal role is the society especially in the field of medicinal chemistry and drug discovery. Particularly its algorithms, neural networks or other recurrent networks drive this area. In this review, we have taken into account the diverse use of AI in a number of pharmaceutical industries including discovery of drugs, repurposing, development of pharmaceutical drug and its clinical trials. In addition, the efficiency of these artificial or machine learning programs in achieving the target drugs in short time period, along with accurate dosage and cost effectively of the drug has also been discussed. Numerous applications of AI in property prediction such as ADMET have been used for prediction of strength of this technology in QSAR. In case of de-novo synthesis, it results in generation of novel drug molecules with unique design proving this a promising field fir drug design. Moreover, its involvement in synthetic planning, ease of synthesis and much more contribute to automated drug discovery in near future.

scholarly journals An Ultrafast and Flexible LC-MS/MS System Paves the Way for Machine Learning Driven Sample Processing and Data Evaluation in Early Drug Discovery

2021 ◽  
Author(s):  
Tim Häbe ◽  
Christian Späth ◽  
Steffen Schrade ◽  
Wolfgang Jörg ◽  
Roderich Süssmuth ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Drug Discovery ◽  
Data Evaluation ◽  
In Vivo Studies ◽  
Integrated Analysis ◽  
Limit Of Quantitation ◽  
Early Drug ◽  
The Way

Rationale: Low speed and flexibility of most LC-MS/MS approaches in early drug discovery delays sample analysis from routine in vivo studies within the same day of measurements. A highthroughput platform for the rapid quantification of drug compounds in various in vivo assays was developed and established in routine bioanalysis. Methods: Automated selection of an efficient and adequate LC method was realized by autonomous sample qualification for ultrafast batch gradients (9 s/sample) or for fast linear gradients (45 s/sample) if samples require chromatography. The hardware and software components of our Rapid and Integrated Analysis System (RIAS) were streamlined for increased analytical throughput via state-of-the-art automation while keeping high analytical quality. Results: Online decision-making was based on a quick assay suitability test (AST) based on a small and dedicated sample set evaluated by two different strategies. 84% of the acquired data points were within ±30% accuracy and 93% of the deviations between the lower limit of quantitation (LLOQ) values were ≤2-fold compared to standard LC-MS/MS systems while speed, flexibility and overall automation was significantly improved. Conclusions: The developed platform provided an analysis time of only 10 min (batch-mode) and 50 min (gradient-mode) per standard pharmacokinetic (PK) study (62 injections). Automation, data evaluation and results handling were optimized to pave the way for machine learning based decision-making regarding the evaluation strategy of the AST

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