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14 papers · ranked by Valyu relevance
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Generation of de novo peptides docking snake three-fold α-neurotoxins (3Ftx) were designed using computational diffusion models as alternatives to screening or designing larger antibody/protein-based and smaller drug-like anti-venoms. To approach the high variability of snake venoms, the previously described…
Authors not listed
Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a…
Jie Lin, Mingyuan Xu, Hongming Chen
Shape-based virtual screening is a widely utilized method in ligand-based de novo drug design, aiming to identify molecules in chemical libraries that share similar 3D shapes but simultaneously possess novel 2D chemical structures compared to the reference compound. As an emerging technology, generative model is an…
Authors not listed
Structure-based drug design (SBDD) is enhanced by machine learning (ML) to improve both virtual screening and de novo design. Despite advances in ML tools for both strategies, screening remains bounded by time and computational cost, while generative models frequently produce invalid and synthetically inaccessible…
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Quantum-Aided Drug Design (QuADD) is a platform that utilizes quantum computing to solve a multi-objective optimization problem, producing novel druglike molecules optimized for interactions within a binding pocket. An alternative approach is that of Generative AI, which has emerged as a powerful tool in drug…
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Free energy calculations have become invaluable in protein design, offering a powerful means to rapidly and accurately screen potential variants. Here, we provide a step-by-step protocol that combines molecular dynamics simulations with non-equilibrium alchemical free energy methods to tackle open questions in protein…
Authors not listed
Bacterial microcompartments (BMCs) are large protein-based organelles found in many bacteria that encapsulate a sequence of enzymes to accelerate specific metabolic pathways and limit toxicity by confining intermediate products. In BMCs to facilitate catalysis, reactant and products must transit across the protein…
Junkil Park, Aseem Partap Singh Gill, Seyed Mohamad Moosavi, JIHAN KIM
The success of diffusion models in the field of image processing has propelled the creation of software such as Dall-E, Midjourney and Stable Diffusion, which are tools used for text-to-image generations. Mapping this workflow onto materials discovery, a new diffusion model was developed for the generation of pure…
Authors not listed
Hydrogen/deuterium exchange (HDX) methods for studying protein dynamics would benefit from millisecond-scale incubations to probe intrinsically disordered proteins, highly dynamic regions and conformation changes. Here we investigate droplet microfluidics for rapid mixing to trigger D2O labelling, uniform incubations…
Authors not listed
The formation and modulation of biomolecular condensates as well as their structural and dynamic properties are determined by an intricate interplay of different driving forces, which down at the microscopic scale involve molecular interactions of the biological macromolecules and the surrounding solvent and ions.…
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We review the use of state-of-the-art enhanced sampling techniques in molecular dynamics (MD) simu- lations. We briefly introduce the principles and practical considerations underlying the application of these methods, making reference to recent reviews, and then discuss their application to membrane transporters as…
Ahmed A. A. I. Ali, Falk Hoffmann, Lars V. Schäfer, Frans A. A. Mulder
Nuclear magnetic resonance (NMR) spin relaxation is the most informative approach to experimentally probe the internal dynamics of proteins on the picosecond to nanosecond timescale. At the same time, molecular dynamics (MD) simulations of biological macromolecules are steadily improving through better physical models…
Authors not listed
While artificial intelligence has revolutionized the prediction of static protein structures, characterizing their dynamics and interactions with drug candidates remains a computational bottleneck. Here, we introduce FeNNix-Bio1, a foundation machine learning model designed to power accurate, reactive atomistic…
Lunna Li, Tommaso Casalini, Paolo Arosio, Matteo Salvalaglio
Intrinsically disordered proteins (IDPs) play a key role in many biological processes, including the formation of biomolecular condensates within cells. A detailed characterization of their configurational ensemble and structure-function paradigm is crucial for understanding their biological activity and for exploiting…