A5061538375- Protein Structure and Dynamics
- Computational Drug Discovery Methods
- Machine Learning in Materials Science
- Insect symbiosis and bacterial influences
- Enzyme Structure and Function
- Machine Learning in Bioinformatics
- Molecular Junctions and Nanostructures
- Mosquito-borne diseases and control
- Various Chemistry Research Topics
- Chronic Lymphocytic Leukemia Research
- SARS-CoV-2 and COVID-19 Research
- Gaussian Processes and Bayesian Inference
- RNA and protein synthesis mechanisms
- Distributed and Parallel Computing Systems
- Protein Degradation and Inhibitors
- Scientific Computing and Data Management
- Chronic Myeloid Leukemia Treatments
- Melanoma and MAPK Pathways
- Virology and Viral Diseases
- Vibrio bacteria research studies
- Fuel Cells and Related Materials
- Quantum, superfluid, helium dynamics
Open Geospatial Consortium
2024-2025
Mucolipidosis IV Foundation
2025
Open Molecular Software Foundation
2024-2025
Memorial Sloan Kettering Cancer Center
2023-2024
University of Edinburgh
2019-2022
Cresset (United Kingdom)
2020
Alchemical free energy calculations are a useful tool for predicting differences associated with the transfer of molecules from one environment to another. The hallmark these methods is use "bridging" potential functions representing \emph{alchemical} intermediate states that cannot exist as real chemical species. data collected bridging alchemical thermodynamic allows efficient computation energies (or in energies) orders magnitude less simulation time than simulating process directly....
Atomic partial charges are crucial parameters in molecular dynamics simulation, dictating the electrostatic contributions to intermolecular energies and thereby potential energy landscape. Traditionally, assignment of has relied on surrogates ab initio semiempirical quantum chemical methods such as AM1-BCC is expensive for large systems or numbers molecules. We propose a hybrid physical/graph neural network-based approximation widely popular charge model that orders magnitude faster while...
A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration energies. The hybrid FEP/ML was trained on a subset of the FreeSolv database and retrospectively shown outperform most submissions from SAMPL4 competition. Compared pure machine-learning approaches, yields more precise estimates energies requires fraction training set size standalone FEP calculations. ML-derived correction terms are...
The Zika viral protease NS2B-NS3 is essential for the cleavage of polyprotein precursor into individual structural and non-structural (NS) proteins therefore an attractive drug target. Generation a robust crystal system co-expressed has enabled us to perform crystallographic fragment screening campaign with 1076 fragments. 47 fragments diverse scaffolds were identified bind in active site protease, another 6 observed potential allosteric site. To identify binding sites that are intolerant...
Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by existence over 80 FDA-approved small-molecule inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused mutations therapeutic target. The advent clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes pairing optimal therapies with mutation profiles. However, efforts hindered lack...
Atomic partial charges are crucial parameters in molecular dynamics (MD) simulation, dictating the electrostatic contributions to intermolecular energies, and thereby potential energy landscape. Traditionally, assignment of has relied on surrogates \textit{ab initio} semiempirical quantum chemical methods such as AM1-BCC, is expensive for large systems or numbers molecules. We propose a hybrid physical / graph neural network-based approximation widely popular AM1-BCC charge model that orders...
Alchemical free energy calculations are becoming an increasingly prevalent tool in drug discovery efforts. Over the past decade, significant progress has been made automating various aspects of this technique. However, one aspect hampering wider application is construction perturbation networks to connect ligands interest. More specifically, ligand pairs with large dissimilarities should be avoided since they can lower convergence and decrease accuracy. Here, we propose a technique for...
Small-molecule lead optimisation in early-stage drug discovery is broadly supported by computational chemistry approaches throughout industry. Over the last decade, Free Energy Perturbation (FEP) has grown into a mature physics-based tool that prospectively guides medicinal decision-making accurately predicting ligand potencies at level of precision required for granular nature stage. Machine-learned ligand-protein co-folding models are forefront accurate protein structure prediction and...
Small-molecule lead optimisation in early-stage drug discovery is broadly supported by computational chemistry approaches throughout industry. Over the last decade, Free Energy Perturbation (FEP) has grown into a mature physics-based tool that prospectively guides medicinal decision-making accurately predicting ligand potencies at level of precision required for granular nature stage. Machine-learned ligand-protein co-folding models are forefront accurate protein structure prediction and...
Alchemical free energy calculations are a useful tool for predicting differences associated with the transfer of molecules from one environment to another. The hallmark these methods is use "bridging" potential functions representing _alchemical_ intermediate states that cannot exist as real chemical species. data collected bridging alchemical thermodynamic allows efficient computation energies (or in energies) orders magnitude less simulation time than simulating process directly. While...
A data-driven approach for predicting networks affinity calculations offers a new route automated molecular simulations in drug discovery.
The Zika virus (ZIKV), discovered in Africa 1947, swiftly spread across continents, causing significant concern due to its recent association with microcephaly newborns and Guillain-Barré syndrome adults. Despite a decrease prevalence, the potential for resurgence remains, necessitating urgent therapeutic interventions. Like other flaviviruses, ZIKV presents promising drug targets within replication machinery, notably NS3 helicase (NS3
Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by existence over 80 FDA-approved small-molecule inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused mutations therapeutic target. The advent clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes pairing optimal therapies with mutation profiles. However, efforts hindered lack...
A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration energies. The hybrid FEP/ML was trained on a subset of the FreeSolv database, and retrospectively shown outperform most submissions from SAMPL4 competition. Compared pure machine-learning approaches, yields more precise estimates energies hydration, requires fraction training set size standalone FEP calculations. ML-derived correction...
Relative binding free energy (RBFE) calculations are increasingly used to support the ligand optimisation problem in early-stage drug discovery. Because RBFE frequently rely on alchemical perturbations between ligands a congeneric series, practitioners required estimate an optimal combination of pairwise for each series. networks constitute collection edges chosen such that all (nodes) included network, where edge represents calculation. As there is vast number possible configurations it not...
A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration energies. The hybrid FEP/ML was trained on a subset of the FreeSolv database, and retrospectively shown outperform most submissions from SAMPL4 competition. Compared pure machine-learning approaches, yields more precise estimates energies hydration, requires fraction training set size standalone FEP calculations. ML-derived correction...
Relative binding free energy (RBFE) calculations are increasingly used to support the ligand optimisation problem in early-stage drug discovery. Because RBFE frequently rely on alchemical perturbations between ligands a congeneric series, practitioners required estimate an optimal combination of pairwise for each series. networks constitute collection edges chosen such that all (nodes) included network, where edge represents calculation. As there is vast number possible configurations it not...