Integrating Chemical Information into Reinforcement Learning for Enhanced Molecular Geometry Optimization

Geometry optimization is a crucial step in computational chemistry, and the efficiency of optimization algorithms plays a pivotal role in reducing computational costs. In this study, we introduce a novel reinforcement-learning-based optimizer that surpasses traditional methods in terms of efficiency. What sets our model apart is its ability to incorporate chemical information into the optimization process. By exploring different state representations that integrate gradients, displacements, primitive type labels, and additional chemical information from the SchNet model, our reinforcement learning optimizer achieves exceptional results. It demonstrates an average reduction of about 50% or more in optimization steps compared to the conventional optimization algorithms that we examined when dealing with challenging initial geometries. Moreover, the reinforcement learning optimizer exhibits promising transferability across various levels of theory, emphasizing its versatility and potential for enhancing molecular geometry optimization. This research highlights the significance of leveraging reinforcement learning algorithms to harness chemical knowledge, paving the way for future advancements in computational chemistry

Integrating Chemical Information into Reinforcement Learning for Enhanced Molecular Geometry Optimization

Geometry optimization is a crucial step in computational chemistry, and the efficiency of optimization algorithms plays a pivotal role in reducing computational costs. In this study, we introduce a novel reinforcement-learning-based optimizer that surpasses traditional methods in terms of efficiency. What sets our model apart is its ability to incorporate chemical information into the optimization process. By exploring different state representations that integrate gradients, displacements, primitive type labels, and additional chemical information from the SchNet model, our reinforcement learning optimizer achieves exceptional results. It demonstrates an average reduction of about 50% or more in optimization steps compared to the conventional optimization algorithms that we examined when dealing with challenging initial geometries. Moreover, the reinforcement learning optimizer exhibits promising transferability across various levels of theory, emphasizing its versatility and potential for enhancing molecular geometry optimization. This research highlights the significance of leveraging reinforcement learning algorithms to harness chemical knowledge, paving the way for future advancements in computational chemistry

Media 1: High-resistance liquid-crystal lens array for rotatable 2D/3D autostereoscopic display

Originally published in Optics Express on 10 February 2014 (oe-22-3-2714

A New Photosensitized Oxidation-Responsive Nanoplatform for Controlled Drug Release and Photodynamic Cancer Therapy

Abnormal biochemical alteration such as unbalanced reactive oxygen species (ROS) levels has been considered as a potential disease-specific trigger to deliver therapeutics to target sites. However, in view of their minute variations in concentration, short lifetimes, and limited ranges of action, in situ generation of ROS with specific manipulations should be more effective for ROS-responsive drug delivery. Here we present a new delivery nanoplatform for photodynamic therapy (PDT) with on-demand drug release regulated by light irradiation. Rose bengal (RB) molecules, which exhibit a high yield of ROS generation, were encapsulated in a mixture of chitosan (CTS), poly­(vinyl alcohol) (PVA), and branched polyethylenimine (<i>b</i>PEI) with hydrophobic iron oxide nanoparticles through an oil-in-water emulsion method. The as-prepared magnetic nanoclusters (MNCs) with a tripolymer coating displayed high water dispersibility, efficient cellular uptake, and the cationic groups of CTS and <i>b</i>PEI were effective for RB loading through electrostatic interaction. The encapsulation efficiency of RB in MNCs could be further improved by increasing the amount of short <i>b</i>PEI chains. During the photodynamic process, controlled release of the host molecules (i.e., RB) or guest molecules (i.e., paclitaxel) from the <i>b</i>PEI-based nanoplatform was achieved simultaneously through a photooxidation action sensitized by RB. This approach promises specific payload release and highly effective PDT or PDT combined therapy in various cancer cell lines including breast (MCF-7 and multidrug resistant MCF-7 subline), SKOV-3 ovarian, and Tramp-C1 prostate. In in vivo xenograft studies, the nanoengineered light-switchable carrier also greatly augments its PDT efficacy against multidrug resistant MCF-7/MDR tumor as compared with free drugs. All the above findings suggest that the substantial effects of enhanced drug distribution for efficient cancer therapy was achieved with this smart nanocarrier capable of on demand drug release and delivery, thus exerting its therapeutic activity to a greater extent

Time-dependent effect of quercetin on TNF-α secretion in RAW264.7 macrophages.

<p>Cells were pretreated with 30 μM quercetin for indicated times and then stimulated with 100 ng/ml LPS for 1 h. The level of TNF-α secreted from macrophages was measured by ELISA. Bars are mean ± SD (n = 3). **<i>p</i><0.01 versus LPS alone group.</p

Patient numbers and percentages in the six regions.

<p>Patient numbers and percentages in the six regions.</p

The effective dose per scan and per patient calculated by the DLP survey data and <i>k</i> values.

<p>The effective dose per scan and per patient calculated by the DLP survey data and <i>k</i> values.</p

Means and standard deviations for DLP per scan (mGy·cm/scan), scan frequency per patient (scans/patient), and DLP per patient (mGy·cm/patient) on the six regions of CT examinations.

<p>Means and standard deviations for DLP per scan (mGy·cm/scan), scan frequency per patient (scans/patient), and DLP per patient (mGy·cm/patient) on the six regions of CT examinations.</p

Effective dose (mSv) per scan, per patient, and contribution percentage of collective effective dose for 22 procedures in Taiwan.

<p>Effective dose (mSv) per scan, per patient, and contribution percentage of collective effective dose for 22 procedures in Taiwan.</p

The role of quercetin in TNF-α and IL-1β expression in RAW264.7 macrophages.

<p>Cells were preincubated with different concentrations of quercetin as indicated for 1 h and then stimulated with 100 ng/ml LPS for another 1 h. The mRNA levels of TNF-α (<b>A</b>) and IL-1β (<b>C</b>) were analyzed by quantitative real-time PCR. The levels of TNF-α (<b>B</b>) and IL-1β (<b>D</b>) secreted from macrophages were measured by ELISA. Data are presented as the mean ± SD (n = 3). *<i>p</i><0.05, **<i>p</i><0.01 versus LPS alone group.</p