Property representations and molecular fragmentation of chemical compounds in QSAR modeling

Polański, Jarosław; Duszkiewicz, Roksana

doi:10.1016/j.chemolab.2020.104146

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…In other words, notso-big data is a pragmatic approach replacing big data due to better interpretability or simply when we cannot expand the data space. Chemo-, pharmaco-, pharmaco-economic substance behavior analyses were published using large data available [22][23][24][25]. In molecular design, we can suggest the term all-data available.…”

Section: The Basic Glossary Of Big Data Artificial Intelligence (Ai) ...mentioning

confidence: 99%

A Brief Guide to Big Data in Molecular Design: From Concepts and Definitions to Models

Polanski

2023

Preprint

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How crucial is big data in contemporary molecular design? In this publication we elucidate fundamental concepts and terminology in this field, critically addressing overlooked issues. We thoroughly examine the size, accessibility, quality, and structural aspects of big data alongside the primary methodologies employed for their analysis. Within chemical compounds, properties and descriptors represent two distinct data types, forming the basis for categorizing molecular big data. The primary objective of chemistry is property production, which means we are searching for novel drugs or materials rather than chemical compounds, and big data is the central issue of this philosophy. The increasing availability of data in computer-aided technology propels advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL). Accordingly, a broad chemical audience must comprehend these methods to understand data-centered chemistry. Thus, we aim to systemize big data issues through a simple illustrative framework with fundamental descriptor categories: coding, computer-generated descriptors, and property correlates. Although we employ computer-generated descriptors as big data for predictions, the measured data are irreplaceable for achieving high-quality and reliable outcomes and controlling molecular effects. The scarcity of property data remains a significant hurdle limiting comprehensive studies on the structure-property relationships within big data. Accordingly, guided by pragmatics not-so-big data is an option for drug design. We presented also a brief review of the recent big data literature.

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Section: The Basic Glossary Of Big Data Artificial Intelligence (Ai) ...mentioning

confidence: 99%

A Brief Guide to Big Data in Molecular Design: From Concepts and Definitions to Models

Polanski

2023

Preprint

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“…Essentially, drug or material design requires that these representations be mapped in order to predict the properties of the targeted materials quantitatively or qualitatively. Descriptors can be calculated from a molecular representation, but properties can only be measured, most often for substances [ 1 , 2 ]. Accordingly, the availability of the measured property data is crucial in material design.…”

Section: Introductionmentioning

confidence: 99%

“…Molecular weight (MW), which can be either a measured property, a predicted property or molecular descriptor, is a good example. Even the term chemical compound ambiguously refers to both the molecules or substances in experiments or a virtual in silico environment [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem

Lach

Zhdan

Smoliński

et al. 2021

IJMS

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(1) Background: Properties and descriptors are two forms of molecular in silico representations. Properties can be further divided into functional, e.g., catalyst or drug activity, and material, e.g., X-ray crystal data. Millions of real measured functional property records are available for drugs or drug candidates in online databases. In contrast, there is not a single database that registers a real conversion, TON or TOF data for catalysts. All of the data are molecular descriptors or material properties, which are mainly of a calculation origin. (2) Results: Here, we explain the reason for this. We reviewed the data handling and sharing problems in the design and discovery of catalyst candidates particularly, material informatics and catalyst design, structural coding, data collection and validation, infrastructure for catalyst design and the online databases for catalyst design. (3) Conclusions: Material design requires a property prediction step. This can only be achieved based on the registered real property measurement. In reality, in catalyst design and discovery, we can observe either a severe functional property deficit or even property famine.

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Molecular modelling of compounds used for corrosion inhibition studies: a review

Ebenso

Verma

Olasunkanmi

et al. 2021

Phys. Chem. Chem. Phys.

127

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Property representations and molecular fragmentation of chemical compounds in QSAR modeling

Cited by 3 publications

References 21 publications

A Brief Guide to Big Data in Molecular Design: From Concepts and Definitions to Models

A Brief Guide to Big Data in Molecular Design: From Concepts and Definitions to Models

Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem

Molecular modelling of compounds used for corrosion inhibition studies: a review

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