Training machine learning potentials for reactive systems: A Colab tutorial on basic models.

In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.

Perspective: Reference-Potential Methods for the Study of Thermodynamic Properties in Chemical Processes: Theory, Applications, and Pitfalls.

In silico investigations of enzymatic reactions and chemical reactions in condensed phases often suffer from formidable computational costs due to a large number of degrees of freedom and enormous important volume in phase space. Usually, accuracy must be compromised to trade for efficiency by lowering the reliability of the Hamiltonians employed or reducing the sampling time. Reference-potential methods (RPMs) offer an alternative approach to reaching high accuracy of simulation without much loss of efficiency. In this Perspective, we summarize the idea of RPMs and showcase some recent applications. Most importantly, the pitfalls of these methods are also discussed, and remedies to these pitfalls are presented.

Ursolic acid augments the chemosensitivity of drug-resistant breast cancer cells to doxorubicin by AMPK-mediated mitochondrial dysfunction.

Multidrug resistance remains the major obstacle to successful therapy for breast carcinoma. Ursolic acid (UA), a triterpenoid compound, has been regarded as a potential neoplasm chemopreventive drug in some preclinical studies since it exerts multiple biological activities. In this research, we investigated the role of UA in augmenting the chemosensitivity of drug-resistant breast carcinoma cells to doxorubicin (DOX), and we further explored the possible molecular mechanisms. Notably, we found that UA treatment led to inhibition of cellular proliferation and migration and cell cycle arrest in DOX-resistant breast cancers. Furthermore, combination treatment with UA and DOX showed a stronger inhibitory effect on cell viability, colony formation, and cell migration; induced more cell apoptosis in vitro; and generated a more potent inhibitory effect on the growth of the MCF-7/ADR xenograft tumor model than DOX alone. Mechanistically, UA effectively increased p-AMPK levels and concomitantly reduced p-mTOR and PGC-1α protein levels, resulting in impaired mitochondrial function, such as mitochondrial respiration inhibition, ATP depletion, and excessive reactive oxygen species (ROS) generation. In addition, UA induced a DNA damage response by increasing intracellular ROS production, thus causing cell cycle arrest at the G0/G1 phase. UA also suppressed aerobic glycolysis by prohibiting the expression and function of Glut1. Considered together, our data demonstrated that UA potentiated the susceptibility of DOX-resistant breast carcinoma cells to DOX by targeting energy metabolism through the AMPK/mTOR/PGC-1α signaling pathway, and it is a potential adjuvant chemotherapeutic candidate in MDR breast cancer.

Neural Component Analysis for Key Performance Indicator Monitoring.

The partial least squares (PLS) algorithm is a commonly used key performance indicator (KPI)-related performance monitoring method. To address nonlinear features in the process, this paper proposes neural component analysis (NCA)-PLS, which combines PLS with NCA. (NCA)-PLS realizes all the principles of PLS by introducing a new loss function and a new principal component selection mechanism to NCA. Then, the gradient descent formulas for network training are rederived. NCA-PLS can extract components with large correlations with KPI variables and adopt them for data reconstruction. Simulation tests using a mathematical model and the Tennessee Eastman process show that NCA-PLS can successfully handle nonlinear relationships in process data and that it performs much better than PLS, KPLS, and NCA.

Understanding of chiral site-dependent enantioselective identification on a plasmon-free semiconductor based SERS substrate.

Chiral differentiation is an important topic in diverse fields ranging from pharmaceutics to chiral synthesis. The improvement of sensitivity and the elucidation of the mechanism of chiral recognition are still the two main challenges. Herein, a plasmon-free semiconductive surface-enhanced Raman spectroscopy (SERS) substrate with sensitive chiral recognition ability is proposed for the discrimination of enantiomers. A homochiral environment is constructed by typical π-π stacking between l-tryptophan (l-Trp) and phenyl rings on well-aligned TiO2 nanotubes (TiO2 NTs). Using 3,4-dihydroxyphenylalanine (DOPA) enantiomers as the targets and the chelating interaction of Fe3+-DOPA for the onsite growth of Prussian blue (PB), the enantioselectivity difference between l-DOPA and d-DOPA on the homochiral substrate can be directly monitored from PB signals in the Raman-silent region. By combining the experimental results with molecular dynamic (MD) simulations, it is found that satisfactory enantioselective identification not only requires a homochiral surface but also largely depends on the chiral center environment-differentiated hydrogen-bond formation availability.

Near Infrared Light-Driven Photothermal Effect on Homochiral Au/TiO2 Nanotube Arrays for Enantioselective Desorption.

Chiral enantiomers have different effects on biological processes. Enantiomer separation is significant and necessary. Herein, a photothermal (PT) effect-derived enantioselective desorption strategy based on homochiral Au/TiO2 nanotubes (NTs) is developed. Using 3,4-dihydroxyphenylalanine (DOPA) as the model enantiomer, an obvious selective desorption of L/D-DOPA can be achieved by the NIR light-triggered local temperature enhancement. Molecular docking simulation further verifies that the distinct affinity precipitated by the different hydrogen bonds between homochiral sorbent and target enantiomers is the origin of enantioselective desorption. This desorption strategy provides a green and alternative approach for the selective separation of chiral molecules.

Affordable Ab Initio Path Integral for Thermodynamic Properties via Molecular Dynamics Simulations Using Semiempirical Reference Potential.

Path integral molecular dynamics (PIMD) is becoming a routinely applied method for incorporating the nuclear quantum effect in computer simulations. However, direct PIMD simulations at an ab initio level of theory are formidably expensive. Using the protonated 1,8-bis(dimethylamino)naphthalene molecule as an example, we show in this work that the computational expense for the intramolecular proton transfer between the two nitrogen atoms can be remarkably reduced by implementing the idea of reference-potential methods. The simulation time can be easily extended to a scale of nanoseconds while maintaining the accuracy on an ab initio level of theory for thermodynamic properties. In addition, postprocessing can be carried out in parallel on massive computer nodes. A 545-fold reduction in the total CPU time can be achieved in this way as compared to a direct PIMD simulation at the same ab initio level of theory.

Accelerated Computation of Free Energy Profile at Ab Initio Quantum Mechanical/Molecular Mechanics Accuracy via a Semiempirical Reference Potential. 3. Gaussian Smoothing on Density-of-States.

Calculations of the free energy profile, also known as potential of mean force (PMF), along a chosen collective variable (CV) are now routinely applied in the studies of chemical processes, such as enzymatic reactions and chemical reactions in condensed phases. However, if the ab initio quantum mechanical/molecular mechanics (QM/MM) level of accuracy is required for the PMF, it can be formidably demanding even with the most advanced enhanced sampling methods, such as umbrella sampling. To ameliorate this difficulty, we developed a novel method for the computation of the free energy profile based on the reference-potential method recently, in which a low-level reference Hamiltonian is employed for phase space sampling and the free energy profile can be corrected to the level of interest (the target Hamiltonian) by energy reweighting in a nonparametric way. However, when the reference Hamiltonian is very different from the target Hamiltonian, the calculated ensemble averages, including the PMF, often suffer from numerical instability, which mainly comes from the overestimation of the density-of-states (DoS) in the low-energy region. Stochastic samplings of these low-energy configurations are rare events, and some low-energy conformations may get oversampled in simulations of a finite length. In this work, an assumption of Gaussian distribution is applied to the DoS in each CV bin, and the weight of each configuration is rescaled according to the accumulated DoS. The results show that this smoothing process can remarkably reduce the ruggedness of the PMF and increase the reliability of the reference-potential method.