Synergistic application of molecular docking and machine learning for improved binding pose

Accurate prediction of protein-ligand complex structures is a crucial step in structure-based drug design.Traditional molecular docking methods exhibit limitations in terms of accuracy and sampling space,while relying on machine-learning approaches may lead to invalid conformations.In this study,we propose a novel strategy that combines molecular docking and machine learning methods.Firstly,the protein-ligand binding poses are predicted using a deep learning model.Subsequently,position-restricted docking on predicted binding poses is performed using Uni-Dock,generating physically constrained and valid binding poses.Finally,the binding poses are re-scored and ranked using machine learning scoring functions.This strategy harnesses the predictive power of machine learning and the physical constraints advantage of molecular docking.Evaluation experiments on multiple datasets demonstrate that,compared to using molecular docking or machine learning methods alone,our proposed strategy can significantly improve the success rate and accuracy of protein-ligand complex structure predictions.

The chemical origin of temperature-dependent lithium-ion concerted diffusion in sulfide solid electrolyte Li_(10)GeP_(2)S_(12)

Solid-state batteries have received increasing attention in scientific and industrial communities,which benefits from the intrinsically safe solid electrolytes(SEs).Although much effort has been devoted to designing SEs with high ionic conductivities,it is extremely difficult to fully understand the ionic diffusion mechanisms in SEs through conventional experimental and theoretical methods.Herein,the temperature-dependent concerted diffusion mechanism of ions in SEs is explored through machinelearning molecular dynamics,taking Li_(10)GeP_(2)S_(12) as a prototype.Weaker diffusion anisotropy,more disordered Li distributions,and shorter residence time are observed at a higher temperature.Arrhenius-type temperature dependence is maintained within a wide temperature range,which is attributed to the linear temperature dependence of jump frequencies of various concerted diffusion modes.These results provide a theoretical framework to understand the ionic diffusion mechanisms in SEs and deepen the understanding of the chemical origin of temperature-dependent concerted diffusions in SEs.

Deep potentials for materials science

To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

MOD-Net:A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs

In this paper,we propose a machine learning approach via model-operatordata network(MOD-Net)for solving PDEs.A MOD-Net is driven by a model to solve PDEs based on operator representationwith regularization fromdata.For linear PDEs,we use a DNN to parameterize the Green’s function and obtain the neural operator to approximate the solution according to the Green’s method.To train the DNN,the empirical risk consists of the mean squared loss with the least square formulation or the variational formulation of the governing equation and boundary conditions.For complicated problems,the empirical risk also includes a fewlabels,which are computed on coarse grid points with cheap computation cost and significantly improves the model accuracy.Intuitively,the labeled dataset works as a regularization in addition to the model constraints.The MOD-Net solves a family of PDEs rather than a specific one and is much more efficient than original neural operator because few expensive labels are required.We numerically show MOD-Net is very efficient in solving Poisson equation and one-dimensional radiative transfer equation.For nonlinear PDEs,the nonlinear MOD-Net can be similarly used as an ansatz for solving nonlinear PDEs,exemplified by solving several nonlinear PDE problems,such as the Burgers equation.

Atomic-scale simulations in multi-component alloys and compounds:A review on advances in interatomic potential

Multi-component alloys have demonstrated excellent performance in various applications,but the vast range of possible compositions and microstructures makes it challenging to identify optimized alloys for specific purposes.To overcome this challenge,large-scale atomic simulation techniques have been widely used for the design and optimization of multi-component alloys.The capability and reliability of large-scale atomic simulations essentially rely on the quality of interatomic potentials that describe the interactions between atoms.This work provides a comprehensive summary of the latest advances in atomic simulation techniques for multi-component alloys.The focus is on interatomic potentials,including both conventional empirical potentials and newly developed machine learning potentials(MLPs).The fitting processes for different types of interatomic potentials applied to multi-component alloys are also discussed.Finally,the challenges and future perspectives in developing MLPs are thoroughly addressed.Overall,this review provides a valuable resource for researchers interested in developing optimized multicomponent alloys using atomic simulation techniques.

Substituent-controlled aggregate luminescence:Computational unraveling of S_(1)/S_(0)surface crossing

We computationally investigated the molecular aggregation effects on the excited state deactivation processes by considering both the direct vibrational relaxation and the S0/S1 surface crossing,that is,the minimum energy conical intersection(MECI).Taking classical AIEgens bis(piperidyl)anthracenes(BPAs)isomers and the substituted silole derivatives as examples,we show that the deformation ofMECI always occurs at the atom with greater hole/electron overlap.Besides,the energetic and structural changes of MECI caused by substituent has been investigated.We find that effective substituent such as the addition of the electron-donating groups,which can polarize the distribution of hole/electron density of molecules,will lead to the pyramidalization deformation of MECI occurring at the substituent position and simultaneously reduce the required energy to reach MECI.And MECI is sterically restricted by the surrounding molecules in solid phase,which remarkably hinders the non-radiative decay through surface crossing.Through quantitative computational assessments of the fluorescence quantum efficiency for both solution and solid phases,we elucidate the role of MECI and its dependence on the substitutions through pyramidalization deformation,which give rise to the aggregation-induced emission(AIE)phenomenon for 9,10-BPA,to aggregation-enhance emission(AEE)behavior for 1,4-BPA,and to conventional aggregation-caused quenching(ACQ)for 1,5-BPA.We further verify such mechanism for siloles,for which we found that the substitutions do not change the AIE behavior.Our findings render a general molecular design approach to manipulating the aggregation effect for optical emission.