Enhanced utility of AI/ML methods during lead optimization by inclusion of 3D ligand information

Bleicher, Leo S.; Daelen, Ton van; Honeycutt, J. Dana; Hassan, Moises; Chandrasekhar, Jayaraman; Shirley, William A.; Tsui, Vickie; Schmitz, Uli

doi:10.3389/fddsv.2022.1074797

Cited by 8 publications

(2 citation statements)

References 26 publications

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“…By retrospectively re-identifying drug candidate molecules based on data from intermediate project stages and applying chemical space constraints, researchers demonstrated GTD's capability to refine lead compounds. Moreover, the GTD platform was configured to generate molecules incorporating features from multiple unrelated molecule series, showcasing its application of AI/ML to drug discovery and lead optimization [37][38][39].…”

Section: Traditional Methods Vs Ai-driven Lead Optimizationmentioning

confidence: 99%

Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery

Niazi,

Mariam,

Magoola

2024

Preprint

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Lead optimization in drug discovery is a crucial phase where initial hits are refined into compounds with improved pharmacological properties. While traditional methods rely on manual experimentation and modifications, AI-driven techniques have revolutionized this process by leveraging big data and predictive modeling. This review explores how AI-driven approaches accelerate lead optimization, showcasing examples like deep neural networks and reinforcement learning. Integration of multi-omic data and experimental validation further enhances AI-driven strategies. The future lies in refining these AI methods, democratizing tools, and interdisciplinary collaboration to streamline drug discovery and address medical needs efficiently.

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Section: Traditional Methods Vs Ai-driven Lead Optimizationmentioning

confidence: 99%

Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery

Niazi,

Mariam,

Magoola

2024

Preprint

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“…18,19 This amalgamation enables us to e ciently navigate a vast chemical space, enhancing the likelihood of identifying lead compounds with inhibitory potential. 20,21 This innovative strategy not only expedites the drug discovery process but also harnesses the collective intelligence of ML and in silico methods to re ne the precision and reliability of our predictions. 22…”

Section: Introductionmentioning

confidence: 99%

Unveiling the Inhibitory Potential of Gingerol Substructures against SARS- CoV-2 RBD: An Integrated Ensemble Learning and In Silico Screening Approach

R.,

2023

Preprint

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In the relentless quest for effective treatments against SARS-CoV-2, extensive exploration of potential inhibitors has been underway. In this study, we present an integrated approach combining machine learning and in silico screening to identify promising inhibitors for the SARS-CoV-2 receptor-binding domain (RBD). We harnessed a dataset of Vina scores for 988 gingerol substructures, employing Random Forest (RF) regression as the optimal model to predict Vina scores accurately (R² = 0.77). Virtual screening, both through RF predictions and PyRx, consistently highlighted 14 molecules with inhibitory potential. Pharmacokinetic evaluation, aided by the Bioavailability Radar and a BOILED-Egg simulation, further refined the selection of four leads-G4, G5, G11 and G13 with human intestinal absorption, out of which the P-gp non substrate G13 (PubChem CID: 135196841) can be act as a promising candidate. Molecular docking, molecular dynamics simulations, and Density Functional Theory (DFT) calculations validated the stability and interactions of this compound with the SARS-CoV-2 RBD. Our study offers a streamlined methodology for identifying potential inhibitor, paving the way for further experimental validation.

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Current Approaches and Strategies Applied in First‐in‐class Drug Discovery

Mohammed,

Sagurthi

2024

ChemMedChem

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First‐in‐class drug discovery (FICDD) offers novel therapies, new biological targets and mechanisms of action (MOAs) toward targeting various diseases and provides opportunities to understand unexplored biology and to target unmet diseases. Current screening approaches followed in FICDD for discovery of hit and lead molecules can be broadly categorized and discussed under phenotypic drug discovery (PDD) and target‐based drug discovery (TBDD). Each category has been further classified and described with suitable examples from the literature outlining the current trends in screening approaches applied in small molecule drug discovery (SMDD). Similarly, recent applications of functional genomics, structural biology, artificial intelligence (AI), machine learning (ML), and other such advanced approaches in FICDD have also been highlighted in the article. Further, some of the current medicinal chemistry strategies applied during discovery of hits and optimization studies such as hit‐to‐lead (HTL) and lead optimization (LO) have been simultaneously overviewed in this article.

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Enhanced utility of AI/ML methods during lead optimization by inclusion of 3D ligand information

Cited by 8 publications

References 26 publications

Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery

Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery

Unveiling the Inhibitory Potential of Gingerol Substructures against SARS- CoV-2 RBD: An Integrated Ensemble Learning and In Silico Screening Approach

Current Approaches and Strategies Applied in First‐in‐class Drug Discovery

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