Carmen Esposito

Modular and Interoperable Workflows for Benchmarking Alchemical Binding Free Energy Calculation Methodologies

Anna M. Herz, Maicol Bissaro, Carmen Esposito, Julien Michel

Alchemical free energy methods are gaining traction in computer-aided drug discovery. An expanding array of methodologies is available for the setup, execution, and analysis of relative binding free energy (RBFE) calculations. However, the sharing of algorithms and protocols developed by different organizations …

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Modular and Interoperable Workflows for Benchmarking Alchemical Binding Free Energy Calculation Methodologies

Anna M. Herz, Maicol Bissaro, Carmen Esposito, Julien Michel

Alchemical Free Energy methods are gaining traction in computer-aided drug discovery. An expanding array of methodologies is available for the setup, execution and analysis of relative binding free energy (RBFE) calculations. However, the sharing of algorithms and protocols developed by different organisations …

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Real-World Applications and Experiences of AI/ML Deployment for Drug Discovery

William R. Pitt, Jonathan M. Bentley, Christophe Boldron, Lionel Colliandre et autres

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The role of human error in human robot interaction

Carmen Esposito, Valentina De Simone, Valentina Di Pasquale, Marta Rinaldi et autres

In the context of Human-Robot Interaction (HRI) within manufacturing environments, Human Error (HE) remains a critical factor affecting performance, safety, and efficiency. Understanding and categorizing these errors, essential for the design of safe and efficient collaborative work cells, still remains a research …

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PIRATE: Plundering AlphaFold Predictions to Automate Protein Engineering

Graham M. Richardson, J. Kopec, Carmen Esposito, Tim James

Abstract An important characteristic for many proteins is the presence of flexible loops or linkers known as intrinsically disordered regions. One downside to many of the state-of-the-art disorder prediction approaches is that they require significant computational time for each inference. Here, we …

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Virtual Reality for Training in Assembly and Disassembly Tasks: A Systematic Literature Review

Valentina Di Pasquale, Paolo Cutolo, Carmen Esposito, Benedetta Franco et autres

The evolving landscape of industrial manufacturing is increasingly embracing automation within smart factories. However, the critical role of human operators, particularly in manual assembly and disassembly tasks, remains undiminished. This paper explores the complexities arising from mass customization and remanufacturing, which significantly …

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Scikit-Mol brings cheminformatics to Scikit-Learn

Esben Jannik Bjerrum, Rafał Adam Bachorz, Adrien Bitton, Oh-Hyeon Choung et autres

Scikit-Mol is a open-source toolkit that aims to bridge the gap between two well-established toolkits, RDKit and Scikit-Learn, in order to provide a simple interface for building cheminformatics models. By leveraging the strengths of both RDKit and Scikit-Learn, Scikit-Mol provides a powerful …

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Dissecting in Vitro the Activation of Human Immune Response Induced by Shigella sonnei GMMA

Serena Tondi, Bruna Clemente, Carmen Esposito, Chiara Sammicheli et autres

Generalized Modules for Membrane Antigens (GMMA) are outer membrane exosomes purified from Gram-negative bacteria genetically mutated to increase blebbing and reduce risk of reactogenicity. This is commonly achieved through modification of the lipid A portion of lipopolysaccharide. GMMA faithfully resemble the bacterial …

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GHOST: Adjusting the Decision Threshold to Handle Imbalanced Data in Machine Learning

Carmen Esposito, Gregory A. Landrum, Nadine Schneider, Nikolaus Stiefl et autres

Machine learning classifiers trained on class imbalanced data are prone to overpredict the majority class. This leads to a larger misclassification rate for the minority class, which in many real-world applications is the class of interest. For binary data, the classification threshold …

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Influence of Lipidation on the Folding and Stability of Collagen Triple Helices—An Experimental and Theoretical Study

Jasmine Egli, Carmen Esposito, Mike Müri, Sereina Riniker et autres

The folding of triple-helical collagen, the most abundant protein in nature, relies on the nucleation and propagation along the strands. Hydrophobic moieties are crucial for the folding and stability of numerous proteins. Instead, nature uses for collagen a trimerization domain and cis …

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Combining Machine Learning and Molecular Dynamics to Predict P‑Glycoprotein Substrates

Carmen Esposito, Shuzhe Wang, Udo E. W. Lange, Frank Oellien et autres

Abstract The efflux transporter P-glycoprotein (P-gp) is responsible for the extrusion of a wide variety of molecules, including drug molecules, from the cell. Therefore, P-gp-mediated efflux transport limits the bioavailability of drugs. To identify potential P-gp substrates early in the drug discovery …

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A New Family of Rigid Dienone Musks Challenges the Perceptive Range of the Human Olfactory Receptor OR5AN1

Quanrui Wang, Sereina Riniker, Philip Kraft, Jie Liu et autres

A new family of dienone musks was discovered by alkylation of different aldehydes with but-3-en-1-yn-1-yllithium and subsequent domino reaction of a Saucy–Marbet transfer vinylation–Claisen rearrangement with an intramolecular Diels–Alder reaction, and concluding Lewis acid catalyzed double-bond isomerization. The newly synthesized dienone structures …

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