Structure-Activity Analysis of Tetrahydrofolate Analogs Using Substituent Constants and Regression Analysis

Miller, E. C.; Hansch, Corwin

doi:10.1002/jps.2600560119

Cited by 21 publications

(10 citation statements)

References 20 publications

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“…While AI is not new, neither is the application of AI to drug discovery, especially in modeling generalized structure−activity relationships. Actually, the idea of taking experimental values and using a "descriptor set" for regression goes all the way back to (and possibly even before) Hammett's pioneering formula connecting reaction rates and equilibrium constants for reactions of benzene derivatives 23 and the computer-assisted identification and quantification of physicochemical properties of bioactive molecules by Hansch, 24,25 who is commonly considered the "father of QSAR" as practiced in the pharmaceutical industry. An increasing number of medicinal chemists have applied various AI methods ever since, to address the central challenge of evaluating and predicting the biological effects of chemicals.…”

Section: Brief Introduction To the History Of Artificial Intelligence...mentioning

confidence: 99%

“…While AI is not new, neither is the application of AI to drug discovery, especially in modeling generalized structure–activity relationships. Actually, the idea of taking experimental values and using a “descriptor set” for regression goes all the way back to (and possibly even before) Hammett’s pioneering formula connecting reaction rates and equilibrium constants for reactions of benzene derivatives and the computer-assisted identification and quantification of physicochemical properties of bioactive molecules by Hansch, , who is commonly considered the “father of QSAR” as practiced in the pharmaceutical industry. An increasing number of medicinal chemists have applied various AI methods ever since, to address the central challenge of evaluating and predicting the biological effects of chemicals. , One method worthy of special mention is the pattern recognition approach, − which focuses on the elucidation and examination of patterns shared between chemical entities, relying on the general assumption that compounds with similar structural patterns should have similar physicochemical properties and in vitro biochemical effects. − Early prototypes and implementations of neural networks (e.g., the Perceptron and its improved derivatives) emerged, which had potential as a means of solving such problems.…”

Section: Introductionmentioning

confidence: 99%

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Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

et al. 2019

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Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

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Section: Brief Introduction To the History Of Artificial Intelligence...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

et al. 2019

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“…Many computational methods have been proposed to reduce the rate of clinical failure . Early quantitative structure–activity relationship (QSAR) models made use of regression models to find connections between molecule descriptors and biological properties . Machine learning methods, such as support vector machine algorithms and decision trees, have been applied in drug discovery tasks such as drug-like classification and prediction of absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties. − In recent years, deep learning, an emerging artificial intelligence (AI) technology, has been implemented to accelerate and improve drug discovery process and yield impressive results in applications such as molecular property and activity prediction, − virtual screening, , retrosynthetic analysis, , and de novo drug generation. , Compared with traditional “shallow” machine learning methods, deep learning uses deep neural networks with more than one hidden layer that can represent and learn more complex knowledge.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Drug Discovery

et al. 2020

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The data sets available to train models for in silico drug discovery efforts are often small. Indeed, the sparse availability of labeled data is a major barrier to artificialintelligence-assisted drug discovery. One solution to this problem is to develop algorithms that can cope with relatively heterogeneous and scarce data. Transfer learning is a type of machine learning that can leverage existing, generalizable knowledge from other related tasks to enable learning of a separate task with a small set of data. Deep transfer learning is the most commonly used type of transfer learning in the field of drug discovery. This Perspective provides an overview of transfer learning and related applications to drug discovery to date. Furthermore, it provides outlooks on the future development of transfer learning for drug discovery.

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“…Using AI in drug development is not new, particularly when modeling structure-activity connections. The idea of using a “descriptor set” and experimental values dates back to Hammett's 13 revolutionary method to connect reaction rates and equilibrium constants of benzene derivatives as well as Hansch's computer-assisted identification and measurement of physicochemical features of bioactive molecules, who is known as the “father of QSAR.” 14 15 . Following that, numerous researchers and scientists globally have widely used a variety of AI techniques to tackle the main problem of assessing and forecasting chemical effects on the body.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Computer-Aided Drug Discovery

Kate,

Seth,

Singh

et al. 2023

Drug Res (Stuttg)

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The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribute effectively in solving many challenges and constraints in the traditional development of the drug. Traditionally, large-scale chemical libraries are screened to find one promising medicine. In recent years, more reasonable structure-based drug design approaches have avoided the first screening phases while still requiring chemists to design, synthesize, and test a wide range of compounds to produce possible novel medications. The process of turning a promising chemical into a medicinal candidate can be expensive and time-consuming. Additionally, a new medication candidate may still fail in clinical trials even after demonstrating promise in laboratory research. In fact, less than 10% of medication candidates that undergo Phase I trials really reach the market. As a consequence, the unmatched data processing power of AI systems may expedite and enhance the drug development process in four different ways: by opening up links to novel biological systems, superior or distinctive chemistry, greater success rates, and faster and less expensive innovation trials. Since these technologies may be used to address a variety of discovery scenarios and biological targets, it is essential to comprehend and distinguish between use cases. As a result, we have emphasized how AI may be used in a variety of areas of the pharmaceutical sciences, including in-depth opportunities for drug research and development.

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Structure-Activity Analysis of Tetrahydrofolate Analogs Using Substituent Constants and Regression Analysis

Cited by 21 publications

References 20 publications

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Transfer Learning for Drug Discovery

Artificial Intelligence for Computer-Aided Drug Discovery

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