Molecules In Terms Research Articles

LncRNA FAM83H-AS1 Contributes to the Radio-resistance and Proliferation in Liver Cancer through Stability FAM83H Protein.

Liver cancer (LC) is one of China's most common malignant tumors, with a high mortality rate, ranking third leading cause of death after gastric and esophageal cancer. Recent patents propose the LncRNA FAM83H-AS1 has been verified to perform a crucial role in the progression of LC. LncRNA FAM83H-AS1 has been verified to perform a crucial role in the progression of LC. However, the concrete mechanism remains to be pending further investigation. This study aimed to explore the embedding mechanism of FAM83H-AS1 molecules in terms of radio sensitivity of LC and provide potentially effective therapeutic targets for LC therapy. Quantitative real-time PCR (qRT-PCR) was conducted to measure the transcription levels of genes. Proliferation was determined via CCK8 and colony formation assays. Western blot was carried out to detect the relative protein expression. A xenograft mouse model was constructed to investigate the effect of LncRNA FAM83H-AS1 on tumor growth and radio-sensitivity in vivo. The levels of lncRNA FAM83H-AS1 were remarkably increased in LC. Knockdown of FAM83H-AS1 inhibited LC cell proliferation and colony survival fraction. Deletion of FAM83H-AS1 increased the sensitivity of LC cells to 4 Gy of X-ray radiation. In the xenograft model, radiotherapy combined with FAM83H-AS1 silencing significantly reduced tumor volume and weight. Overexpression of FAM83H reversed the effects of FAM83H-AS1 deletion on proliferation and colony survival fraction in LC cells. Moreover, the over-expressing of FAM83H also restored the tumor volume and weight reduction caused by the knockdown of FAM83H-AS1 or radiation in the xenograft model. Knockdown of lncRNA FAM83H-AS1 inhibited LC growth and enhanced radiosensitivity in LC. It has the potential to be a promising target for LC therapy.

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

Structure-based virtual screening of binding of candidate drug molecules is a topic of increasing interest in the discovery of small molecule drugs. As the same drug molecule may bind to different binding sites on a target protein, the binding site selectivity that is related to the binding tendency of candidate drug molecules to different binding sites after reaching the target protein need to be considered in sufficient details. In this work, a systematic and computer-aided virtual screening framework based on the binding site selectivity to screen candidate drug molecules in terms of their ability to bind on selected sites is presented. The framework integrates two machine learning (ML)-based models to predict the binding potential and binding selectivity to specific binding sites that are important for virtual screening of drug molecules. The details of the ML-based models together with the work-flow of the computer-aided virtual screening methods and the efficient and consistent integration of related drug design tools are presented. The applicability of this virtual screening framework is illustrated through a case study involving the screening for drug molecules as inhibitors to block the binding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to angiotensin converting enzyme 2 (ACE2), which is the target protein. The case study results point to identification of new candidate inhibitors with better binding site selectivity than two known potential inhibitors, Nilotinib and SSAA09E2.

Synthesis of patchy colloids with different chemical functionalities

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

The single crystal 5-Fluorouracil l-phenylalanine (5FUPA) was synthesized using solid state technique and crystallized at room temperature using the slow evaporation method. The crystal with orthorhombic structure and P212121 space group was obtained. Quantum chemical studies were used to compute the structural parameters and various spectroscopic properties for 5FUPA using the B3LYP/6–311++G (d, p). The structural parameters from reported single crystal XRD and calculated from DFT were compared, and they were showing good agreement with each other. The peaks obtained by experimental FTIR and modes calculated by theoretical DFT method have a good degree of consistency. The UV–Visible absorption spectrum of 5FUPA shown to have maximum absorption peak at 266 nm. The NBO analysis was used to calculate the interaction between bonding and anti-bonding orbitals and their stabilization energy in 5FUPA. The most reactive sites of the 5FUPA cocrystal were investigated by Fukui function descriptor and Molecular Electrostatic Potential (MEP) energy surface calculated using Mulliken charges. Inter- and intra-molecular interactions of the 5FUPA were investigated using the Hirshfeld surface and finger print methods. The topological studies ELF, LOL and thermodynamic parameters at different temperatures were performed. The molecular docking outcomes indicate that, cocrystal 5FUPA have better inhibitory effects on breast cancer proteins when compared to 5-fluorouracil and l-phenylalanine. The 5FUPA cocrystal has performed better compared to the other two molecules in terms of docking affinity, having a better binding score range of -8.3 kcal/mol to -9.4 kcal/mol. This demonstrates that 5FUPA cocrystal has unveiled more bioactivity and reactivity when compared with 5-fluorouracil and l-phenylalanine, and can be further considered as a breast cancer medication after clinical trials.

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

The density functionals M06–2X and B3LYP were exploited for investigating the impact of modifying the surface of a boron nitride nano-tube (BNNT) with the furan molecule in terms of energy, electronics, and geometry. According to the density functional theory (DFT) results, BNNT had a strong interaction with the furan molecule. So, the adhesion of the furan molecule onto the surface of BNNT was consistent with the chemical functionalization. Furthermore, the computed density of states indicated that the electronic features of the BNNT were adjusted with a partial chemical modification. Also, the functional energy raised by enhancing the electron donation property of the functional groups, which was due to the rise in the electrical conductance of BNNT in contrast with the pristine BNNT. Retaining the electronic features of BNNT and the increased solubility demonstrated that using furan to chemically modify BNNTs could be a useful method to purify them.

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

A molecular imprinted polymeric (MIP) recognition system comprising of PEDOT swaddled CoWO4 nanocapsules has been developed for the electrochemical detection of carcinogenic phenacetin drug. Facile hydrothermal route was employed to synthesize CoWO4 whereas, EDOT was wrapped around under chemical polymerization at room temperature. A series of characterizations revealed that electrostatic interactions generated between thiophene sulfur of PEDOT and metal atoms of CoWO4 have prompted the homogeneously wrapped molecularly imprinted nanocomposite. The obtained peak potential plotted against the CV scan rate revealed involvement of 2 electrons in the electroxidation detection process of phenacetin. The values of charge transfer coefficient α (0.69) and charge transfer rate constant ks (2.24 s−1) at electrode-electrolyte interface during the detection procedure were also evaluated. The nanoarchitecture of the designed MIP exhibited abundant imprinted sites to selectively bind the template molecule in terms of shape, size and functionalities resulted in significant augmentation in sensitivity (0.14 µA nM−1 cm−2) and a very low detection limit (0.091 nM). Phenacetin content in homeopathic and allopathic drug samples were successfully quantified with more than 95 % accuracy by employing the MIP modified electrode. The present research introduces new possibilities for identification of carcinogenic phenacetin used as adulterants in various narcotic 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

Ozone is one of the important molecules in terms of the impact on the atmospheric chemistry, climate changes, bio- and eco-systems and human health. It has a strong absorption in the microwave, terahertz and far-infrared spectral ranges where a large part of the Earth’s outgoing longwave radiation to space is located. In this work, the observations, and analyses of the ozone high-resolution spectra in the THz range recorded using the Synchrotron light source of the SOLEIL CNRS equipment are reported for the first time. Thanks to the exceptional brightness of the Synchrotron radiation and to the signal/noise ratio, it was possible to observe many more ozone transitions of the cold rotational band and the hot ν2-ν2 band in the range 0.9–6 THz compared to the previous works. In addition, we have carried out new measurements and assignments for the ν2 band. The simultaneous fit of the rotational band GS-GS, the hot band ν2-ν2 and the FIR ν2 band yielded an overall weighted standard deviation of 0.68 for 13,466 line positions within the experimental accuracy. This includes all previously available MW (with the best uncertainty 0.1 – 10 kHz), FIR data and the original SOLEIL measurements that provided experimental accuracy of 0.00005 – 0.0001 cm−1 for the best lines. Significant deviations in new experimental spectra compared to available spectroscopic databases were evidenced, particularly for the line positions and energy levels at high J, Ka rotational quantum numbers that are the most pronounced in the 4.5 – 6 THz range. Accurate ab initio calculations of line intensities combined with empirically fitted line positions were used to create new linelists that permit theoretical modelling of the transmittance in a good agreement with the Synchrotron spectra in the entire range of observations for various pressures and optical paths. The region near 100 cm−1 and above appears to be more sensitive to the temperature conditions that should be considered in atmospheric observation for the currently operational and future ground based and space missions.

Personalized medicine in the dish to prevent calcium leak associated with short-coupled polymorphic ventricular tachycardia in patient-derived cardiomyocytes

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.

Computational exploration of the structure-photophysical property relationship of 2,6-dicyanoaniline derivatives for optoelectronic applications

Design and development of 2,6-Dicyanoaniline based multifunctional organic molecules with an acceptor-donor-acceptor (A-D-A) framework is gaining momentum in recent years. However, identifying the suitable donor, acceptor and π-linkers remains challenging and necessitates a thorough understanding of the structure-property relationship. In this context, to enhance their properties, two experimentally reported dicyanoaniline (DCA) derivatives are subjected to structural modifications using four electron-releasing groups: OMe, NH2, NHMe, and NMe2, employing density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations. Our findings reveal that all the designed molecules exhibit higher reactivity compared to the reference molecules, with NMe2 showing the most promising attributes, including a lower HOMO-LUMO gap. Percentage contribution analysis reveals that the strength of electron releasing groups significantly alter the stabilization of HOMO. Absorption and emission spectra are obtained from TD-DFT calculations in dimethyl formamide solvent. Moreover, calculations are performed to explore the Nonlinear Optical (NLO) performance of the designed molecules, indicating that all the designed molecules outperform the reference molecules in terms of NLO properties. In summary, this study offers valuable guidance for the development of efficient 2,6-Dicyanoaniline derivatives with great potential for optoelectronic and NLO applications.

Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE.

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.

A Big Data Approach to Rational Design and Discovery of Electrolytes

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.

Theoretical spectroscopy for unraveling the intensity mechanism of the optical and photoluminescent spectra of chiral Re(I) transition metal complexes.

In this work, we present a computational study that is able to predict the optical absorption and photoluminescent properties of the chiral Re(I) family of complexes [fac-ReX(CO)3L], where X is either Cl or I and L is N-heterocyclic carbene extended with π-conjugated [5]-helicenic unit. The computational strategy is based on carefully calibrated time dependent density functional theory calculations and operates in conjunction with an excited state dynamics approach to treat in addition to absorption (ABS) and photoluminescence (PL), electronic circular dichroism (ECD), and circularly polarized luminescence (CPL) spectroscopies, respectively. The employed computational approach provides, an addition, access to the computation of phosphorescence rates in terms of radiative and non-radiative relaxation processes. The chosen molecules consist of representative examples of non-helicenic (NHC) and helicenic diastereomers. The agreement between theoretical and experimental spectra, including absorption (ABS, ECD) and emission (PL, CPL), is excellent, validating a quantitative interpretation of the spectral features on the basis of natural transition orbitals and TheoDore analyses. It is demonstrated that across the set of studied Re(I) diastereomers, the emission process in the case of NHC diastereomers is metal to ligand charge transfer in nature and is dominated by the easy-axis anisotropy of the emissive excited multiplet. On the contrary, in the cases of the helicenic diastereomers, the emission process is intra ligand charge transfer in nature and is dominated by the respective easy-plane anisotropy of the emissive excited multiplet. This affects remarkably the photoluminescent properties of the molecules in terms of PL and CPL spectral band shapes, spin-vibronic coupling, relaxation times, and the respective quantum yields. Spin-vibronic coupling effects are investigated at the level of the state-average complete active space self-consistent field in conjunction with quasi-degenerate second order perturbation theory. It is in fact demonstrated that a spin-vibronic coupling mechanism controls the observed photophysics of this class of Re(I) complexes.

A Critical View over the Newest Antidiabetic Molecules in Light of Efficacy-A Systematic Review and Meta-Analysis.

The increase in life expectancy without a decrease in the years lived without disability leads to the rise of the population aged over 65 years prone to polypharmacy. The novel antidiabetic drugs can improve this global therapeutic and health problem in patients with diabetes mellitus (DM). We aimed to establish the efficacy (A1c hemoglobin reduction) and safety of the newest antidiabetic drugs (considered so due to their novelty in medical practice use), specifically DPP-4i, SGLT-2i, GLP-1 Ra, and tirzepatide. The present meta-analysis followed the protocol registered at Prospero with the CRD42022330442 registration number. The reduction in HbA1c in the DPP4-i class for tenegliptin was 95% CI -0.54 [-1.1, 0.01], p = 0.06; in the SGLT2-iclass for ipragliflozin 95% CI -0.2 [-0.87, 0.47], p = 0.55; and for tofogliflozin 95% CI 3.13 [-12.02, 18.28], p = 0.69, while for tirzepatide it was 0.15, 95% CI [-0.50, 0.80] (p = 0.65). The guidelines for treatment in type 2 DM are provided from cardiovascular outcome trials that report mainly major adverse cardiovascular events and data about efficacy. The newest antidiabetic non-insulinic drugs are reported to be efficient in lowering HbA1c, but this effect depends between classes, molecules, or patients' age. The newest antidiabetic drugs are proven to be efficient molecules in terms of HbA1c decrease, weight reduction, and safety, but more studies are needed in order to characterize exactly their efficacy and safety profiles.

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

Numerous studies have demonstrated the role of T helper (Th) 17 and T regulatory (reg) cells and pro-inflammatory and anti-inflammatory cytokines related to these cells in the pathogenesis of MS and its animal model, experimental autoimmune encephalomyelitis (EAE). STAT3 is one of the downstream signaling proteins of IL-23, IL-6, and IL-21 that are required for Th17 cells differentiation. STA-21 is a STAT3 inhibitor that functions by inhibiting STAT3 dimerization and binding to DNA impairing the expression of STAT3 target genes including, RORγt, IL-21 and IL-23R that are also required for Th17 cell differentiation. In this study, we evaluated the effect of STA-21 on EAE Model and investigated how this small molecule can change Th17/Treg balance leading to amelioration of disease. After EAE induction and treatment with STA-21, its effects were assessed. Major assays were H&E and LFB staining, Flow cytometric analysis, Reverse transcription-PCR (RT-PCR), and ELISA. STA-21 ameliorated the EAE severity and decreased the EAE inflammation and demyelination. It also decreased STAT3 phosphorylation, the proportion of Th17 cells and the protein level of IL-17. In contrast, the balance of Tregs and the level of anti-inflammatory cytokine, IL-10 increased in STA-21-treated mice. Moreover, STA-21 significantly decreased the expression of Th17 related transcription factors, RORɣt and IL-23R while FOXP3 expression associated with Treg differentiation was increased. This study showed that STA-21 has therapeutic effects in EAE by reducing inflammation and shifting inflammatory immune responses to anti-inflammatory and can be used as a suitable treatment strategy for the treatment of EAE. The effectiveness of inhibiting or strengthening the functional cells of the immune system by these small molecules in terms of easy to access, simple construction and inexpensive expansion make them as a suitable tool for the treatment of inflammatory and autoimmune diseases.

Molecular Graph Generation by Decomposition and Reassembling.

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.

Are Coarse-Grained Structures as Good as Atomistic Ones for Calculating the Electronic Properties of Organic Semiconductors?

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.

Recently developed glycosphingolipid probes and their dynamic behavior in cell plasma membranes as revealed by single-molecule imaging.

Glycosphingolipids, including gangliosides, are representative lipid raft markers that perform a variety of physiological roles in cell membranes. However, studies aimed at revealing their dynamic behavior in living cells are rare, mostly due to a lack of suitable fluorescent probes. Recently, the ganglio-series, lacto-series, and globo-series glycosphingolipid probes, which mimic the behavior of the parental molecules in terms of partitioning to the raft fraction, were developed by conjugating hydrophilic dyes to the terminal glycans of glycosphingolipids using state-of-art entirely chemical-based synthetic techniques. High-speed, single-molecule observation of these fluorescent probes revealed that gangliosides were scarcely trapped in small domains (100nm in diameter) for more than 5ms in steady-state cells, suggesting that rafts including gangliosides were always moving and very small. Furthermore, dual-color, single-molecule observations clearly showed that homodimers and clusters of GPI-anchored proteins were stabilized by transiently recruiting sphingolipids, including gangliosides, to form homodimer rafts and the cluster rafts, respectively. In this review, we briefly summarize recent studies, the development of a variety of glycosphingolipid probes as well as the identification of the raft structures including gangliosides in living cells by single-molecule imaging.

Designing Electron-Deficient Diketone Unit Based Non-Fused Ring Acceptors with Amplified Optoelectronic Features for Highly Efficient Organic Solar Cells: A DFT Study.

Organic solar cells (OSCs) made of electron-acceptor and electron-donor materials have significantly developed in the last decade, demonstrating their enormous potential in cutting-edge optoelectronic applications. Consequently, we designed seven novel non-fused ring electron acceptors (NFREAs) (BTIC-U1 to BTIC-U7) using synthesized electron-deficient diketone units and reported end-capped acceptors, a viable route for augmented optoelectronic properties. The DFT and TDDFT approaches were used to measure the power conversion efficiency (PCE), open circuit voltage (Voc), reorganization energies (λh, λe), fill factor (FF), light harvesting efficiency (LHE) and to evaluate the potential usage of proposed compounds in solar cell applications. The findings confirmed that the photovoltaic, photophysical, and electronic properties of the designed molecules BTIC-U1 to BTIC-U7 are superior to those of reference BTIC-R. The TDM analysis demonstrates a smooth flow of charge from the core to the acceptor groups. Charge transfer analysis of the BTIC-U1:PTB7-Th blend revealed orbital superposition and successful charge transfer from HOMO (PTB7-Th) to LUMO (BTIC-U1). The BTIC-U5 and BTIC-U7 outperformed the reference BTIC-R and other developed molecules in terms of PCE (23.29% and 21.18%), FF (0.901 and 0.894), normalized Voc (48.674 and 44.597), and Voc (1.261 eV and 1.155 eV). The proposed compounds enclose high electron and hole transfer mobilities, making them the ideal candidate for use with PTB7-Th film. As a result, future SM-OSC design should prioritize using these constructed molecules, which exhibit excellent optoelectronic properties, as superior scaffolds.

Designing of Thiophene [3, 2-b] Pyrrole Ring-Based NFAs for High-Performance Electron Transport Materials: A DFT Study.

Among the blended components of a photoactive layer in organic photovoltaic (OPV) cells, the acceptor is of high importance. This importance is attributed to its increased ability to withdraw electrons toward itself for their effective transport toward the respective electrode. In this research work, seven new non-fullerene acceptors were designed for their possible utilization in the OPVs. These molecules were designed through side-chain engineering of the PTBTP-4F molecule, with its fused pyrrole ring-based donor core and different strongly electron-withdrawing acceptors. To elucidate their effectiveness, the band gaps, absorption characteristics, chemical reactivity indices, and photovoltaic parameters of all of the architecture molecules were compared with the reference. Through various computational software, transition density matrices, graphs of absorption, and density of states were also plotted for these molecules. From some chemical reactivity indices and electron mobility values, it was proposed that our newly designed molecules could be better electron-transporting materials than the reference. Among all, TP1, due to its most stabilized frontier molecular orbitals, lowest band gap and excitation energies, highest absorption maxima in both the solvent and gas medium, least hardness, highest ionization potential, superior electron affinity, lowest electron reorganization energy, as well as highest rate constant of charge hopping, seemed to be the best molecule in terms of its electron-withdrawing abilities in the photoactive layer blend. In addition, in terms of all of the photovoltaic parameters, TP4-TP7 was perceived to be better suited in comparison to TPR. Thus, all our suggested molecules could act as superior acceptors to TPR.

On the Best Way to Cluster NCI-60 Molecules

Machine learning-based models have been widely used in the early drug-design pipeline. To validate these models, cross-validation strategies have been employed, including those using clustering of molecules in terms of their chemical structures. However, the poor clustering of compounds will compromise such validation, especially on test molecules dissimilar to those in the training set. This study aims at finding the best way to cluster the molecules screened by the National Cancer Institute (NCI)-60 project by comparing hierarchical, Taylor-Butina, and uniform manifold approximation and projection (UMAP) clustering methods. The best-performing algorithm can then be used to generate clusters for model validation strategies. This study also aims at measuring the impact of removing outlier molecules prior to the clustering step. Clustering results are evaluated using three well-known clustering quality metrics. In addition, we compute an average similarity matrix to assess the quality of each cluster. The results show variation in clustering quality from method to method. The clusters obtained by the hierarchical and Taylor-Butina methods are more computationally expensive to use in cross-validation strategies, and both cluster the molecules poorly. In contrast, the UMAP method provides the best quality, and therefore we recommend it to analyze this highly valuable dataset.