Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study

Siddiqui, Gohar; Stebani, Julia; Wragg, Darren; Koutsourelakis, Phaedon‐Stelios; Casini, Angela; Gagliardi, Alessio

doi:10.1002/chem.202302375

Cited by 3 publications

(2 citation statements)

References 65 publications

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“…Interestingly, machine learning (ML) approaches have been implemented to accelerate metadynamic simulations and free-energy calculations using AuTMX 2 /G4 adducts as model systems. 67 This work paves the way to key applications of ML in drug discovery, enriching the toolbox of methods available for computer-aided drug design (CADD) beyond quantitative structure−activity relationship (QSAR) analysis, virtual screening, and de novo drug design. Further validation of the multimodal MoA of AuTMX 2 via noncovalent interactions has been achieved by shot-gun proteomics in ovarian cancer cells.…”

Section: Noncovalent Organometallic Bindersmentioning

confidence: 99%

“…Structural characterization of the binding modes of AuTMX 2 with different G4s was achieved by both X-ray diffraction studies and atomistic simulations (Figure F), evidencing the importance of π–π stacking and possibly electrostatic interactions in stabilizing the Au(I) compound/G4 adducts. Interestingly, machine learning (ML) approaches have been implemented to accelerate metadynamic simulations and free-energy calculations using AuTMX 2 /G4 adducts as model systems . This work paves the way to key applications of ML in drug discovery, enriching the toolbox of methods available for computer-aided drug design (CADD) beyond quantitative structure–activity relationship (QSAR) analysis, virtual screening, and de novo drug design.…”

Section: Anticancer Organometallic Drugsmentioning

confidence: 99%

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Metals in Cancer Research: Beyond Platinum Metallodrugs

Casini,

Pöthig

2024

ACS Cent. Sci.

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The discovery of the medicinal properties of platinum complexes has fueled the design and synthesis of new anticancer metallodrugs endowed with unique modes of action (MoA). Among the various families of experimental antiproliferative agents, organometallics have emerged as ideal platforms to control the compounds' reactivity and stability in a physiological environment. This is advantageous to efficiently deliver novel prodrug activation strategies, as well as to design metallodrugs acting only via noncovalent interactions with their pharmacological targets. Noteworthy, another justification for the advance of organometallic compounds for therapy stems from their ability to catalyze bioorthogonal reactions in cancer cells.When not yet ideal as drug leads, such compounds can be used as selective chemical tools that benefit from the advantages of catalytic amplification to either label the target of interest (e.g., proteins) or boost the output of biochemical signals. Examples of metallodrugs for the so-called "catalysis in cells" are considered in this Outlook together with other organometallic drug candidates. The selected case studies are discussed in the frame of more general challenges in the field of medicinal inorganic chemistry.

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Section: Noncovalent Organometallic Bindersmentioning

confidence: 99%

Section: Anticancer Organometallic Drugsmentioning

confidence: 99%

Metals in Cancer Research: Beyond Platinum Metallodrugs

Casini,

Pöthig

2024

ACS Cent. Sci.

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Machine learning-based prediction of DNA G-quadruplex folding topology with G4ShapePredictor

Liew,

Lim,

Yong

2024

Sci Rep

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Deoxyribonucleic acid (DNA) is able to form non-canonical four-stranded helical structures with diverse folding patterns known as G-quadruplexes (G4s). G4 topologies are classified based on their relative strand orientation following the 5’ to 3’ phosphate backbone polarity. Broadly, G4 topologies are either parallel (4+0), antiparallel (2+2), or hybrid (3+1). G4s play crucial roles in biological processes such as DNA repair, DNA replication, transcription and have thus emerged as biological targets in drug design. While computational models have been developed to predict G4 formation, there is currently no existing model capable of predicting G4 folding topology based on its nucleic acid sequence. Therefore, we introduce G4ShapePredictor (G4SP), an application featuring a collection of multi-classification machine learning models that are trained on a custom G4 dataset combining entries from existing literature and in-house circular dichroism experiments. G4ShapePredictor is designed to accurately predict G4 folding topologies in potassium ( ) buffer based on its primary sequence and is able to incorporate a threshold optimization strategy allowing users to maximise precision. Furthermore, we have identified three topological sequence motifs that suggest specific G4 folding topologies of (4+0), (2+2) or (3+1) when utilising the decision-making mechanisms of G4ShapePredictor.

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Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Serrano,

Luciano,

Anaya

et al. 2024

Pharmaceutics

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Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI’s transformative impact on the pharmaceutical industry and its broader implications for healthcare.

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Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study

Cited by 3 publications

References 65 publications

Metals in Cancer Research: Beyond Platinum Metallodrugs

Metals in Cancer Research: Beyond Platinum Metallodrugs

Machine learning-based prediction of DNA G-quadruplex folding topology with G4ShapePredictor

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

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