The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials

Oselusi, Samson O.; Dube, Phumuzile; Odugbemi, Adeshina I.; Akinyede, Kolajo A.; Ilori, Tosin L.; Egieyeh, Elizabeth; Sibuyi, Nicole RS.; Meyer, Mervin; Madiehe, Abram M.; Wyckoff, Gerald J.; Egieyeh, Samuel A.

doi:10.1016/j.compbiomed.2024.107927

Cited by 11 publications

(4 citation statements)

References 119 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Models have been applied to accelerate the discovery and design of drug candidates against different multidrug-resistant microbes. They may significantly cut the conventional cost and screening efforts required for the process of screening for new drug candidates [164].…”

Section: Drug Discoverymentioning

confidence: 99%

See 1 more Smart Citation

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Rusic,

Kumric,

Seselja Perisin

et al. 2024

Microorganisms

View full text Add to dashboard Cite

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs’ kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

show abstract

Section: Drug Discoverymentioning

confidence: 99%

“…New antimicrobials can outperform existing compounds as they can evade resistance, possess anti-biofilm activities, and have new modes of action. Furthermore, AI can make estimations on further efficacy, resistance trends, and adverse reactions, thus prioritising the most promising compounds [164,194].…”

Section: Drug Discoverymentioning

confidence: 99%

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Rusic,

Kumric,

Seselja Perisin

et al. 2024

Microorganisms

View full text Add to dashboard Cite

show abstract

“…In the realm of scientific research, in silico methods have become indispensable, offering a diverse array of computational tools applicable across scientific domains spanning drug discovery, materials science, and beyond. These methods harness the capabilities of algorithms and simulations to scrutinize and predict molecular interactions, thereby providing valuable insights into the behavior of molecules under varied conditions. , Of particular significance in recent times is the burgeoning interest in computer-aided drug discovery (CADD) approaches, driven by their potential to address the scale, time, and cost challenges inherent in conventional experimental methodologies. CADD encompasses a series of computational steps, including the identification of potential drug targets, the virtual screening of extensive chemical libraries for promising drug candidates, the subsequent optimization of candidate compounds, and in silico assessments of their potential toxicity.…”

Section: Introductionmentioning

confidence: 99%

Unveiling a Novel Solvatomorphism of Anti-inflammatory Flufenamic Acid: X-ray Structure, Quantum Chemical, and In Silico Studies

Chimatahalli Shanthakumar,

Sridhara,

Rajabathar

et al. 2024

ACS Omega

View full text Add to dashboard Cite

This paper delves into the polymorphism of 2- [3-(trifluoromethyl)anilino]benzoic acid, commonly referred to as flufenamic acid (FA), a pharmaceutical agent employed in treating inflammatory conditions. The central focus of the study is on a newly unearthed solvatomorphic structure of FA in methanol (FAM), and a thorough comparison is conducted with the commercially available standard structure. Employing a comprehensive approach, including X-ray crystallography, Hirshfeld surface analysis, density functional theory (DFT), molecular docking, and molecular dynamics (MD) simulations, the research aims to unravel the structural and functional implications of solvatomorphism. The X-ray crystal structure analysis brings to light notable differences between the standard FA and solvatomorphic FAM, showcasing variations in intermolecular interactions and crystal packing. Key features such as hydrogen bonding, π•••π stacking, and C−H•••π interactions are identified as influential factors shaping the stability and conformation of the compounds. Hirshfeld surface analysis further quantifies the nature and contribution of intermolecular interactions, providing a comprehensive perspective on molecular stability. Density functional theory offers valuable electronic structure insights, highlighting disparities in frontier molecular orbitals between FA and FAM. Molecular docking studies against prostaglandin D2 11-ketoreductase explore potential drug interactions, unveiling distinct binding modes and hydrogen bonding patterns that shed light on how the solvatomorphic structure may impact drug−target interactions. In-depth molecular dynamics simulations over 100 ns investigate the stability of the protein−ligand complex, with root mean square deviation and root mean square fluctuation analyses revealing minimal deviations and affirming the stability of FAM within the active site of the target protein.

show abstract

“…With the rapid advancement of artificial intelligence, computer-aided drug design (CADD) screening holds the potential to significantly reduce the substantial costs associated with conventional drug development . It can predict, screen, and lead optimize compounds based on their structure or potential pharmacological groups. , Based on its primary focus on compound structures and the screening properties related to target receptors, CADD enables seamless alignment with the property alterations resulting from the structural modifications of curcumin . The application of CADD enables high-throughput screening of various distinct characteristics arising from structural modifications of curcumin derivatives.…”

Section: Introductionmentioning

confidence: 99%

Targeted Screening of Curcumin Derivatives as Pancreatic Lipase Inhibitors Using Computer-Aided Drug Design

Jing,

Luo,

Zeng

et al. 2024

ACS Omega

View full text Add to dashboard Cite

The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials

Cited by 11 publications

References 119 publications

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Unveiling a Novel Solvatomorphism of Anti-inflammatory Flufenamic Acid: X-ray Structure, Quantum Chemical, and In Silico Studies

Targeted Screening of Curcumin Derivatives as Pancreatic Lipase Inhibitors Using Computer-Aided Drug Design

Contact Info

Product

Resources

About