Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

Abstract

There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.

Polo-like Kinase 1 Inhibitors in Human Cancer Therapy: Development and Therapeutic Potential

“…Therefore, designing specialized dual-target inhibitors and not relying on their “accidental drug discovery” may become a trend in the development of PLK1i against the KD. In this respect, the linker pharmacophore approach as well as AI-based approaches will be avenues for their future development . As the dose-limiting toxicity of many kinase inhibitors is due primarily to the unspecific activity of these inhibitors, improving PLK1 specificity may be a major strategy that needs to be pursued to achieve better clinical outcomes.…”

“…In this respect, the linker pharmacophore approach as well as AI-based approaches will be avenues for their future development. 212 As the dose-limiting toxicity of many kinase inhibitors is due primarily to the unspecific activity of these inhibitors, 213 improving PLK1 specificity may be a major strategy that needs to be pursued to achieve better clinical outcomes. Further development of ATPcompetitive inhibitors can be designed through structure-guided medicinal chemistry targeting unique PLK1 residues in the ATP-binding pocket, such as F58 and R134.…”

Machine Learning in Antibacterial Drug Design

“…However, common problems with big data sources such as data quality, over-fitting, and difficult or lengthy protocols should be taken in consideration ( Motamedi et al, 2022 ). Taken together, the big data era will walk hand-in-hand with future drug design and will have a significant impact on how to approach a drug discovery campaign ( Zhu, 2020 ; Bhattarai, et al, 2022 ; Lee et al, 2022 ). Zhao et al point out in a wonderful report “10 Vs.” or characteristics that are intrinsic in drug discovery big data that we should be aware of and utilize, namely: volume (size of data), velocity (data growth), variety (lots of data sources), veracity (variable data quality), validity (authenticity of data), vocabulary (aware of the terminology), venue (numerous data platforms), visualization (presentation and patterns in data), volatility (time domain of the data and usefulness time window), and value (associated economic and added value, Zhao et al, 2020 ).…”

ViSAS for Entering Chemical Space: Virtual Screening of Analog Series and Related Advances

“…In a recent example, Steadman et al [98] reported a dockingbased virtual screening to identify new inhibitors of Notum, a negative regulator of Wnt signaling. They screened several successful series and found the [1,2,4] With the rise of large and ultra-large chemical databases, virtual screening has evolved as a natural way to exploit their contents and diversity. [99,100] Besides a database to search in, virtual screening requires additional information, for example, the receptor's structure and a force field for docking scoring (example of a structure-based approach) or known ligands and a system for assessing similarity (example of a ligandbased approach).…”

“…Through computer-aided drug design (CADD) and recently with new artificial intelligence (AI) techniques, it has been possible to accelerate the generation of knowledge from big data in biological, chemical and pharmaceutical medicine. [1] The methods developed in CADD, which have been optimized with machine learning (ML) algorithms, can use the vast chemical space combined with its biological information to obtain compounds with safety, efficacy, and low toxicity, a goal in many drug design projects.…”