A virtual screening framework based on the binding site selectivity for small molecule drug discovery

Patchy particles are one of the new frontiers in developing novel functional colloids because of their similarity to molecules in terms of their potential ability to form directional bonds and give rise to complex architecture via self-assembly. Considerable challenges remain in developing effective strategies for synthesizing such complex particles. Most current works have focused on controlling the position of patches, which is already tricky. Still, very few works have tackled the problem of preparing particles with patches bearing different functionalities. In this work, we used an activated swelling method developed in our group to prepare Janus polymer particles with variable Janus balance and used them to synthesize bi-patchy particles with different chemical functionalities. We first created polystyrene/polymethyl methacrylate-glycidyl methacrylate (PS/P(MMA-GMA)) Janus particles with a small methacrylate patch. The epoxy groups were then aminated (PS/P-NH2), leading to a change in the hydrophilicity of the patch. We then used two methods to create patchy particles. In the first case, we mixed unmodified PS/P(MMA-GMA) and PS/P-NH2 Janus particles and fused them by adding THF as a plasticizer. This led to the formation of bi-patchy particles with identical patches and two patches bearing different functionalities, as proved by fluorescence microscopy. In the second case, PS/P-NH2 particles were used as seeds for an additional activated swelling polymerization step with MMA as a monomer to obtain pure bi-patchy particles with differently functionalized patches. This was possible thanks to the incompatibility between the NH2-functionalized first patch and the pure PMMA of the second one. The work opens the path to preparing colloidal molecules capable of forming multiple directional bonds through orthogonal chemical functionalization.

Solid state synthesis of zwitterionic cocrystal 5-fluorouracil L-phenylalanine explicated via structural, spectroscopy, quantum chemicals, Hirshfeld and docking analysis

Chemical modifications of boron nitride nanotubes with heterocyclic molecules: A DFT study

PEDOT wrapped biomimetic recognition system for selective determination of carcinogenic phenacetin content in drug samples

Ozone spectroscopy in the terahertz range from first high-resolution Synchrotron SOLEIL experiments combined with far-infrared measurements and ab initio intensity calculations

BackgroundPolymorphic ventricular tachycardia (PMVT) is a rare genetic disease associated with structurally normal hearts which in 8% of cases can lead to sudden cardiac death, typically exercise-induced. We previously showed a link between the RyR2-H29D mutation and a clinical phenotype of short-coupled PMVT at rest using patient-specific hiPSC-derived cardiomyocytes (hiPSC-CMs). In the present study, we evaluated the effects of clinical and experimental anti-arrhythmic drugs on the intracellular Ca2+ handling, contractile and molecular properties in PMVT hiPSC-CMs in order to model a personalized medicine approach in vitro.MethodsPreviously, a blood sample from a patient carrying the RyR2-H29D mutation was collected and reprogrammed into several clones of RyR2-H29D hiPSCs, and in addition we generated an isogenic control by reverting the RyR2-H29D mutation using CRIPSR/Cas9 technology. Here, we tested 4 drugs with anti-arrhythmic properties: propranolol, verapamil, flecainide, and the Rycal S107. We performed fluorescence confocal microscopy, video-image-based analyses and biochemical analyses to investigate the impact of these drugs on the functional and molecular features of the PMVT RyR2-H29D hiPSC-CMs.ResultsThe voltage-dependent Ca2+ channel inhibitor verapamil did not prevent the aberrant release of sarcoplasmic reticulum (SR) Ca2+ in the RyR2-H29D hiPSC-CMs, whereas it was prevented by S107, flecainide or propranolol. Cardiac tissue comprised of RyR2-H29D hiPSC-CMs exhibited aberrant contractile properties that were largely prevented by S107, flecainide and propranolol. These 3 drugs also recovered synchronous contraction in RyR2-H29D cardiac tissue, while verapamil did not. At the biochemical level, S107 was the only drug able to restore calstabin2 binding to RyR2 as observed in the isogenic control.ConclusionsBy testing 4 drugs on patient-specific PMVT hiPSC-CMs, we concluded that S107 and flecainide are the most potent molecules in terms of preventing the abnormal SR Ca2+ release and contractile properties in RyR2-H29D hiPSC-CMs, whereas the effect of propranolol is partial, and verapamil appears ineffective. In contrast with the 3 other drugs, S107 was able to prevent a major post-translational modification of RyR2-H29D mutant channels, the loss of calstabin2 binding to RyR2. Using patient-specific hiPSC and CRISPR/Cas9 technologies, we showed that S107 is the most efficient in vitro candidate for treating the short-coupled PMVT at rest.

This work introduces a three-dimensional (3D) invariant graph-to-string transformer variational autoencoders (VAE) (Vagrant) for generating molecules with accurate density functional theory (DFT)-level properties. Vagrant learns to model the joint probability distribution of a 3D molecular structure and its properties by encoding molecular structures into a 3D-aware latent space. Directed navigation through this latent space implicitly optimizes the 3D structure of a molecule, and the latent embedding can be used to condition a generative transformer to predict the candidate structure as a one-dimensional (1D) sequence. Additionally, we introduce two novel sampling methods that exploit the latent characteristics of a VAE to improve performance. We show that our method outperforms comparable 3D autoregressive and diffusion methods for predicting quantum chemical property values of novel molecules in terms of both sample quality and computational efficiency.

Lithium-metal batteries (LMBs) have much higher energy densities than the current state-of-the-art lithium-ion batteries that drive the green energy landscape. However, their wide-scale commercialization is hampered by the lack of desirable electrolytes that suffer from stability and cyclability issues. The discovery of electrolytes for LMBs has been mainly guided by chemical intuition and trial-and-error experiments. While the emerging machine learning (ML)-based initiatives for electrolyte discovery are promising, most studies have been carried out on limited datasets of ionic conductivity, they remain experimentally unvalidated, and lack in design principles governing performance of efficient electrolytes. The present work addresses these problems by curating exhaustive datasets of the most critical parameters affecting the LMB electrolytes – ionic conductive, oxidative stability, and Coulombic efficiency and by developing highly accurate ML and deep learning (DL) models. The models were consistent with the known chemical principles of ionic conductivity and pinpointed few non-trivial trends. After undergoing stringent checks on out-of-distribution (OOD) in-house data sets for the three target properties, the ML models were finally deployed on large unlabeled datasets to identify new and promising electrolytes, and a score was devised to rank the molecules in terms of the predicted target properties. The subsequent experimental investigations led to an entirely new class of LMB electrolytes, establishing the efficacy of our heuristic approach. The proposed “multi-pronged” strategy for LMB electrolyte discovery will also enable researchers to find extraordinary electrolytes for other battery chemistries.

It is impractical to develop a new parts collection forevery potentialhost organism. It is well-established that gene expression parts,like genes, are qualitatively transferable, but there is little quantitativeinformation defining transferability. Here, we systematically quantifiedthe behavior of a parts set across multiple hosts. To do this, wedeveloped a broad host range (BHR) plasmid system compatible withthe large, modular CIDAR parts collection for E. coli, which we named openCIDAR. This enabled testing of a library ofDNA constructs across the Pseudomonadota—Escherichiacoli, Pseudomonas putida, Cupriavidus necator, and Komagataeibacternataicola. Part performance was evaluated with a standardizedcharacterization procedure that quantified expression in terms ofmolecules of equivalent fluorescein (MEFL), an objective unit of measure.The results showed that the CIDAR parts enable graded gene expressionacross all organisms—meaning that the same parts can be usedto program E. coli, P. putida, C. necator, and K. nataicola. Most parts had a similar expression trend across hosts, althougheach organism had a different average gene expression level. The variabilityis enough that to achieve the same MEFL in a different organism, alookup table is required to translate a design from one host to another.To identify truly divergent parts, we applied linear regression toa combinatorial set of promoters and ribosome binding sites, findingthat the promoter J23100 behaves very differently in K. nataicola than in the other hosts. Thus, it is now possible to evaluate anyCIDAR compatible part in three other hosts of interest, and the diversityof these hosts implies that the collection will also be compatiblewith many other Proteobacteria (Pseudomonadota). Furthermore, thiswork defines an approach to generalize modular synthetic biology partssets beyond a single host, implying that only a few parts sets maybe needed to span the tree of life. This will accelerate current effortsto engineer diverse species for environmental, biotechnological, andhealth applications.

STA-21, a small molecule STAT3 inhibitor, ameliorates experimental autoimmune encephalomyelitis by altering Th-17/Treg balance.

Designing molecular structures with desired chemical properties is an essential task in drug discovery and materials design. However, finding molecules with the optimized desired properties is still a challenging task due to combinatorial explosion of the candidate space of molecules. Here we propose a novel decomposition-and-reassembling-based approach, which does not include any optimization in hidden space, and our generation process is highly interpretable. Our method is a two-step procedure: In the first decomposition step, we apply frequent subgraph mining to a molecular database to collect a smaller size of subgraphs as building blocks of molecules. In the second reassembling step, we search desirable building blocks guided via reinforcement learning and combine them to generate new molecules. Our experiments show that our method not only can find better molecules in terms of two standard criteria, the penalized log P and druglikeness, but also can generate drug molecules showing the valid intermediate molecules.

The quality of amorphous molecular morphologies obtained with a recently introduced coarse-grained model, representing molecules in terms of connected anisotropic beads (Phys. Chem. Chem. Phys. 2019, 21, 26195), is benchmarked against reference atomistic data. Typical small-molecule organic semiconductors in their pristine and doped forms are chosen as a challenging and technologically relevant case study for our comparison, which includes both structural features and the resulting electronic properties, such as charge carrier energy levels, energetic disorder, and intermolecular charge transfer couplings. Our analysis shows that our accurate coarse-grained model leads to molecular glasses that are very similar to native atomistic samples, with the discrepancy being further reduced upon back-mapping. The electronic properties computed for back-mapped morphologies are almost indistinguishable from the atomistic reference, especially for multibranched poly(hetero)cyclic hydrocarbons usually employed as organic semiconductors. This study provides a proof of principle for highly accurate large-scale simulations of complex molecular systems at a reduced computational cost.

Abstract Unlike triplet diradicals, singlet diradicals can vary in diradical character from 0 % to 100 % depending on linker units that allow two formally unpaired electrons to couple covalently. In principle, the electronic structure of singlet diradicals can be described as a quantum superposition of closed-shell and open-shell structures. This means that, depending on the external environment, singlet diradicals can behave as either closed-shell or open-shell species. This paper summarizes our progress in understanding the electronic structure of π-conjugated singlet diradical molecules in terms of closed-shell and open-shell dual nature. We first discuss the coexistence of intra- and intermolecular covalent bonding interactions in the π-dimer of a singlet diradical molecule. The intra- and intermolecular coupling of two formally unpaired electrons are related to closed-shell and open-shell nature of singlet diradical, respectively. Then we demonstrate the coexistence of the covalent bonding interactions in the one-dimensional stack of singlet diradical molecules having different diradical character. The relative strength of the interactions is varied with the magnitude of singlet diradical index y 0. Finally, we show the dual reactivity of a singlet diradical molecule, which undergoes rapid [4 + 2] and [4 + 4] cycloaddition reactions in the dark at room temperature. Closed-shell and open-shell nature endow the singlet diradical molecule with the reaction manner as diene and diradical species, respectively.

Three multifunctional targeted NI-BODIPYs (10-12) and GO-(10-12) nanocarriers were fabricated. NI-BODIPYs are designed to facilitate non-covalent interaction with graphene oxide (GO) and target toward cancer cells for specific recognition with glucose moieties while efficiently producing singlet oxygen. We probed detailed characterization, fundamental photophysical/photochemical properties, and interactions with GO of such triplet photosensitizers and nanocarriers. The effect of the formation of nanohybrids with GO on singlet oxygen formation as well as on the efficacies of the molecules in terms of in vitro killing of cancer cells was evaluated with K562 human chronic myelogenous leukemia cells. Amazingly, it was observed that GO exhibited favorable interactions with the NI-BODIPY dyads and promoted the formation of singlet oxygen, while not showing any dark toxicity.

Efficient side-chain engineering of thieno-imidazole salt-based molecule to boost the optoelectronic attributes of organic solar cells: A DFT approach

Despite considerable progress, the issues for precise evaluation of the surface-enhanced Raman scattering (SERS) substrate enhancement factor (EF) have not yet been completely solved until now. Herein, we verify that key aspects of probe molecules including initial concentration, footprint, and diffusion kinetics need to be taken into account for the experimental SERS EF measurement. The three factors collectively affect the surface coverage and thereby the determination of the number of probe molecules on the SERS substrate. We recommend that the measurement of the SERS EFs should be performed with complete surface coverage of probe molecules at steady-state equilibrium using relatively high initial concentrations. It has been demonstrated for the first time that the SERS EFs are independent of the initial concentration of probe molecules when exceeding the threshold initial concentration for complete surface coverage. This study highlights the importance of probe molecules in terms of diffusion kinetics and surface coverage for the accurate determination of the SERS EFs, which has yet to be addressed in the literature.

This review provides an overview covering mRNA from its use in the COVID-19 pandemic to cancer immunotherapy, starting from the selection of appropriate antigens, tumor-associated and tumor-specific antigens, neoantigens, the basics of optimizing the mRNA molecule in terms of stability, efficacy, and tolerability, choosing the best formulation and the optimal route of administration, to summarizing current clinical trials of mRNA vaccines in tumor therapy.

EccBase: A high-quality database for exploration and characterization of extrachromosomal circular DNAs in cancer

The S-H bond tunneling predissociation dynamics of thiophenol and its ortho-substituted derivatives (2-fluorothiophenol, 2-methoxythiophenol, and 2-chlorothiphenol) in S1 (ππ*) where the H atom tunneling is mediated by the nearby S2 (πσ*) state (which is repulsive along the S-H bond extension coordinate) have been investigated in a state-specific way using the picosecond time-resolved pump-probe spectroscopy for the jet-cooled molecules. The effects of the specific vibrational mode excitations and the SH/SD substitutions on the S-H(D) bond rupture tunneling dynamics have been interrogated, giving deep insights into the multidimensional aspects of the S1/S2 conical intersection, which also shapes the underlying adiabatic tunneling potential energy surfaces (PESs). The semiclassical tunneling rate calculations based on the Wentzel-Kramers-Brillouin (WKB) approximation or Zhu-Nakamura (ZN) theory have been carried out based on the ab initio PESs calculated in the (one, two, or three) reduced dimensions to be compared with the experiment. Though the quantitative experimental results could not be reproduced satisfactorily by the present calculations, the qualitative trends among different molecules in terms of the behavior of the tunneling rate versus the (adiabatic) barrier height or the number of PES dimensions could be rationalized. Most interestingly, the H/D kinetic isotope effect observed in the tunneling rate could be much better explained by the ZN theory compared to the WKB approximation, indicating that the nonadiabatic coupling matrix elements should be invoked for understanding the tunneling dynamics taking place in the proximity of the conical intersection.