Feasibility of Cultivating Artemisia afra Jacq. ex Willd in Côte d’Ivoire (Daloa) and Evaluation of Its Genetic Diversity on the Basis of Phenotypic Variations

In the southern African regions, Artemisia afra Jacq. ex Willd is one of the most popular and commonly used herbal medicines. In recent years, A. afra has received much attention from the scientific community and its use is being investigated in the modern diseases like diabetes, cardiovascular diseases, cancer, and respiratory diseases. This growth in popularity could pose a threat to the species due to intensive harvesting. Indeed, overexploitation is a growing problem for many medicinal species in Africa. To sustain the production and availability of A. afra, cultivation seems to be a good strategy and an alternative to collecting in the wild. Unlike A. annua L. (source of artemisinin), little information is available on the cultivation of A. Afra in West African countries. In this study, feasibility of cultivating A. Afra in Côte d’Ivoire was evaluated and the extent of its genetic diversity was assessed based on morphological variations. A. annua L. was used as control. The result showed for A. afra, 30 and 28.02% nursery and field mortality respectively, and 27.77% and 0% for A. annua. A. annua showed faster growth and development kinetics during the first 90 days after field transplantation. A. annua was relatively earlier (83 days to flowering on average) than A. afra (207.20 days to flowering on average). Contrary to A. annua, A. afra was sterile and did not give viable seeds, which poses a major problem of acclimatization in the environmental conditions of Côte d’Ivoire. Assessment of morphological traits revealed significant variations within and between species. Multivariate analysis showed important intra and interspecific genetic diversity. The plants of A. afra and A. annua were grouped separately and six major clusters were found: two clusters in A. annua (cluster I and II) and four clusters in A. afra (cluster III, IV, V and VI). These results show that further studies need to be considered to make cultivation of A. afra possible in Côte d’Ivoire with superior and genetically stable genotypes. Keywords: Artemisia afra, Artemisia annua, genetic diversity, cultivation DOI: 10.7176/JBAH/10-24-04 Publication date: December 31st 202

Adaptive variational quantum algorithms on a noisy intermediate scale quantum computer

Hybrid quantum-classical algorithms hold the potential to outperform classical computing methods for simulating quantum many-body systems. Adaptive Variational Quantum Eigensolvers (VQE) in particular have demonstrated an ability to generate highly accurate ansatz wave-functions using compact quantum circuits. However, the practical implementation of these methods on current quantum processing units (QPUs) faces significant challenges: the requirement to measure a polynomially scaling number of observables during the operator selection step, followed by the need to optimize a high-dimensional, noisy cost-function. In this study, we introduce new techniques to overcome these difficulties and execute hybrid adaptive algorithms on a 25-qubit error-mitigated quantum hardware coupled to a high performance GPU-accelerated quantum simulator. As a physics application, we compute the ground state of a 25-body Ising model using a novel greedy ADAPT-VQE procedure that requires only five circuit measurements for each iteration, regardless of the number of qubits and the size of the operator pool. As a chemistry application, we combine this greedy approach with the Overlap-ADAPT-VQE algorithm to approximate the ground state of a molecular system. The successful implementation of these hybrid QPU/simulator computations enhances the applicability of adaptive VQE algorithms on QPUs and instills further optimism regarding the near-term advantages of quantum computing

Blood flow reduced-order modeling across macroscopic through mesoscopic scales

International audienceWe propose a hemodynamic reduced-order model bridging macroscopic and mesoscopic blood flowcirculation scales from small arteries to capillaries. Representative network geometries are generatedby means of stochastic growth algorithms constrained by statistical morphological and topologicalprinciples and are mathematically described by graphs. Different compliant structural models withrespect to pressure loads are used depending on vessel walls thicknesses and structures. Nonlinearrheological properties of blood are also included in the model. Dynamic network responses are computedfor different conditions. The computational model quantifies small-scale flow pulsatility, whichhas wide-ranging physiological influences

Accurate Deep Learning-aided Density-free Strategy for Many-Body Dispersion-corrected Density Functional Theory

Using a Deep Neuronal Network model (DNN) trained on the large ANI-1 data set of small organic molecules, we propose a transferable density-free many-body dispersion model (DNN-MBD). The DNN strategy bypasses the explicit Hirshfeld partitioning of the Kohn-Sham electron density required by MBD models to obtain the atom-in-molecules volumes used by the Tkatchenko-Scheffler polarizability rescaling. The resulting DNN-MBD model is trained with minimal basis iterative Stockholder atomic volumes and, coupled to Density Functional Theory (DFT), exhibits comparable (if not greater) accuracy to other approaches based on different partitioning schemes. Implemented in the Tinker-HP package, the DNN-MBD model decreases the overall computational cost compared to MBD models where the explicit density partitioning is performed. Its coupling with the recently introduced Stochastic formulation of the MBD equations (J. Chem. Theory. Comput., 2022, 18, 3, 1633-1645) enables large routine dispersion-corrected DFT calculations at preserved accuracy. Furthermore, the DNN electron density-free features extend MBD's applicability beyond electronic structure theory within methodologies such as force fields and neural networks

Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including Long-range Effects

Deep-HP is a scalable extension of the Tinker-HP multi-GPUs molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Networks (DNNs) models. Deep-HP increases DNNs MD capabilities by orders of magnitude offering access to ns simulations for 100k-atom biosystems while offering the possibility of coupling DNNs to any classical (FFs) and many-body polarizable (PFFs) force fields. It allows therefore to introduce the ANI-2X/AMOEBA hybrid polarizable potential designed for ligand binding studies where solvent-solvent and solvent-solute interactions are computed with the AMOEBA PFF while solute-solute ones are computed by the ANI-2x DNN. ANI-2X/AMOEBA explicitly includes AMOEBA's physical long-range interactions via an efficient Particle Mesh Ewald implementation while preserving ANI-2X's solute short-range quantum mechanical accuracy. The DNNs/PFFs partition can be user-defined allowing for hybrid simulations to include biosimulation key ingredients such as polarizable solvents, polarizable counter ions, etc... ANI-2X/AMOEBA is accelerated using a multiple-timestep strategy focusing on the models contributions to low-frequency modes of nuclear forces. It primarily evaluates AMOEBA forces while including ANI-2x ones only via correction-steps resulting in an order of magnitude acceleration over standard Velocity Verlet integration. Simulating more than 10 μs, we compute charged/uncharged ligands solvation free energies in 4 solvents, and absolute binding free energies of host-guest complexes from SAMPL challenges. ANI-2X/AMOEBA average errors are within chemical accuracy opening the path towards large-scale hybrid DNNs simulations, at force-field cost, in biophysics and drug discovery

An Efficient GaMD Multi-Level Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

We introduce a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field.By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple--timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual--water approach is tested on the 1D Potential of Mean Force (PMF) of the solvated CD2--CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling--US--GaMD (ASUS-GaMD) scheme. The highly parallel ASUS--GaMD setup decreases time to convergence by respectively 10 and 20 times compared to GaMD-US and US. Overall, beside the acceleration of PMF computations, Tinker-HP now allows for the simultaneous use of Adaptive Sampling and GaMD-"dual water" enhanced sampling approaches increasing the applicability of polarizable force fields to large scale simulations of biological systems

Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including Long-range Effects

Deep-HP is a scalable extension of the Tinker-HP multi-GPUs molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Networks (DNNs) models. Deep-HP increases DNNs MD capabilities by orders of magnitude offering access to ns simulations for 100k-atom biosystems while offering the possibility of coupling DNNs to any classical (FFs) and many-body polarizable (PFFs) force fields. It allows therefore to introduce the ANI-2X/AMOEBA hybrid polarizable potential designed for ligand binding studies where solvent-solvent and solvent-solute interactions are computed with the AMOEBA PFF while solute-solute ones are computed by the ANI-2x DNN. ANI-2X/AMOEBA explicitly includes AMOEBA's physical long-range interactions via an efficient Particle Mesh Ewald implementation while preserving ANI-2X's solute short-range quantum mechanical accuracy. The DNNs/PFFs partition can be user-defined allowing for hybrid simulations to include biosimulation key ingredients such as polarizable solvents, polarizable counter ions, etc... ANI-2X/AMOEBA is accelerated using a multiple-timestep strategy focusing on the models contributions to low-frequency modes of nuclear forces. It primarily evaluates AMOEBA forces while including ANI-2x ones only via correction-steps resulting in an order of magnitude acceleration over standard Velocity Verlet integration. Simulating more than 10 μs, we compute charged/uncharged ligands solvation free energies in 4 solvents, and absolute binding free energies of host-guest complexes from SAMPL challenges. ANI-2X/AMOEBA average errors are within chemical accuracy opening the path towards large-scale hybrid DNNs simulations, at force-field cost, in biophysics and drug discovery

Tinker-HP: Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields Using GPUs and Multi-GPU Systems

International audienceWe present the extension of the Tinker-HP package (Lagardere, et al. Chem. Sci. 2018, 9, 956−972) to the use of Graphics Processing Unit (GPU) cards to accelerate molecular dynamics simulations using polarizable many-body force fields. The new highperformance module allows for an efficient use of single-and multiple-GPU architectures ranging from research laboratories to modern supercomputer centers. After detailing an analysis of our general scalable strategy that relies on OPENACC and CUDA, we discuss the various capabilities of the package. Among them, the multiprecision possibilities of the code are discussed. If an efficient double precision implementation is provided to preserve the possibility of fast reference computations, we show that a lower precision arithmetic is preferred providing a similar accuracy for molecular dynamics while exhibiting superior performances. As Tinker-HP is mainly dedicated to accelerate simulations using new generation point dipole polarizable force field, we focus our study on the implementation of the AMOEBA model. Testing various NVIDIA platforms including 2080Ti, 3090, V100, and A100 cards, we provide illustrative benchmarks of the code for single-and multicards simulations on large biosystems encompassing up to millions of atoms. The new code strongly reduces time to solution and offers the best performances to date obtained using the AMOEBA polarizable force field. Perspectives toward the strong-scaling performance of our multinode massive parallelization strategy, unsupervised adaptive sampling and large scale applicability of the Tinker-HP code in biophysics are discussed. The present software has been released in phase advance on GitHub in link with the High Performance Computing community COVID-19 research efforts and is free for Academics (see https://github.com/ TinkerTools/tinker-hp)

Interfacial Water Many-body Effects Drive Structural Dynamics and Allosteric interactions in SARS-CoV-2 Main Protease Dimerization Interface

Following our previous work (Chem. Sci., 2021, 12, 4889 – 4907), we study the structural dynamics of the SARS-CoV-2 Main Protease dimerization interface (apo dimer) by means of microsecond adaptive sampling molecular dynamics simulations (50 microseconds) using the AMOEBA polarizable force field (PFF). This interface is structured by a complex H-bond network that is only stable at physiological pH. Structural correlations analysis between its residues and the catalytic site confirms the presence of a buried allosteric site. However, noticeable differences in allosteric connectivity are observed between PFFs and non-PFFs. Interfacial polarizable water molecules are shown to appear at the heart of this discrepancy, since they are connected to the global interface H-bond network and able to adapt their dipole moment (and dynamics) to their diverse local physico-chemical micro-environments. The water-interface many-body interactions appear to drive the interface volume fluctuations and to therefore mediate the allosteric interactions with the catalytic cavity