Analog Design Research Articles

Design, Synthesis, Computational Studies and Evaluation of Novel Hybrid Bipyrimidine Analogues for Anticancer Activity against MCF-7 and A549 Cell Lines

A series of novel hybrid bipyrimidine derivatives were designed, synthesized and evaluated for their anticancer activity against the breast cancer cell line MCF-7 and the lung cancer cell line A549. The structures of all the synthesized molecules were confirmed using various spectroscopic techniques. Molecular docking studies were conducted using Maestro software from Schrödinger, targeting the crystal structure of the ErbB4 kinase (PDB ID: 3BBT). Lapatinib and gefitinib were used as reference drugs to compare binding affinities with the ErbB family of receptor tyrosine kinases. Several of the synthesized bipyrimidine derivatives demonstrated promising activity, with IC[Formula: see text] values comparable to the standard drug doxorubicin and gefitinib. Notably, compound PP-10 exhibited equipotent effects when compared to the reference drugs in both breast and lung cancer cell lines. Additionally, compounds PP-07 and PP-09 showed significant potency against the MCF-7 cell line, while compounds PP-02, PP-09, PP-11 and PP-14 were active against the A549 cell line. These novel hybrid bipyrimidine derivatives served as potential lead molecules in the development of novel drug-like molecules for the treatment of breast and lung cancer.

Predicting Antitumor Activity of Anthrapyrazole Derivatives using Advanced Machine Learning Techniques.

Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors. The present study introduces novel QSAR models for the prediction of antitumor activity of anthrapyrazole analogues. The predictive performance of four machine learning algorithms, namely artificial neural networks, boosted trees, multivariate adaptive regression splines, and random forest, was studied in terms of variation of the observed and predicted data, internal validation, predictability, precision, and accuracy. ANN and boosted trees algorithms met the validation criteria. It means that these procedures may be able to forecast the anticancer effects of the anthrapyrazoles studied. Evaluation of validation metrics, calculated for each approach, indicated the artificial neural network (ANN) procedure as the algorithm of choice, especially with regard to the obtained predictability as well as the lowest value of mean absolute error. The designed multilayer perceptron (MLP)-15-7-1 network displayed a high correlation between the predicted and the experimental pIC50 value for the training, test, and validation set. A conducted sensitivity analysis enabled an indication of the most important structural features of the studied activity. The ANN strategy combines topographical and topological information and can be used for the design and development of novel anthrapyrazole analogues as anticancer molecules.

Design, Synthesis, Molecular Docking and Hypoglycemic Activity of Novel Coumarin Analogues

AbstractDiabetes and its associated conditions are significant causes of morbidity and mortality. Despite the abundance of available medications for diabetes treatment, their limitations have spurred ongoing research in this field. Consequently, there is a need to discover a potential therapeutic molecule for managing diabetes and its complications. In current research work a series of novel coumarin analogs (5a–5e) were designed, synthesized, characterized through FTIR, 1H NMR, 13C NMR, and mass spectroscopy and evaluated for antidiabetic property. Molecular docking study was performed to link its binding affinity toward AMPK. DPPH scavenging assay was performed for in vitro antioxidant activity, for reference standard ascorbic acid was taken. In vivo hypoglycemic activity was performed on streptozotocin and nicotinic acid induced diabetic rats. All synthesized molecule showed good docking, score among them compound 5b has lowest docking score −11.25 kcal/mol. Molecule 5b and 5c showed greater antioxidant potential with good IC50 value, i.e., 20.4 g/mL and 21.6 g/mL. In vivo antidiabetic study showed good control on blood glucose level, cholesterol, triglyceride, Hb1AC as well as SGOT and SGPT level on animals fed 5b when compared to metformin. This result showed that test molecule 5b has potential to lower the blood glucose level without causing any toxicity.

BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors.

Identifying small molecules that bind strongly to target proteins in rational molecular design is crucial. Machine learning techniques, such as generative adversarial networks (GAN), are now essential tools for generating such molecules. In this study, we present an enhanced method for molecule generation using objective-reinforced GANs. Specifically, we introduce BEGAN (Boltzmann-enhanced GAN), a novel approach that adjusts molecule occurrence frequencies during training based on the Boltzmann distribution exp(-ΔU/τ), where ΔU represents the estimated binding free energy derived from docking algorithms and τ is a temperature-related scaling hyperparameter. This Boltzmann reweighting process shifts the generation process toward molecules with higher binding affinities, allowing the GAN to explore molecular spaces with superior binding properties. The reweighting process can also be refined through multiple iterations without altering the overall distribution shape. To validate our approach, we apply it to the design of sex pheromone analogs targeting Spodoptera frugiperda pheromone receptor SfruOR16, illustrating that the Boltzmann reweighting significantly increases the likelihood of generating promising sex pheromone analogs with improved binding affinities to SfruOR16, further supported by atomistic molecular dynamics simulations. Furthermore, we conduct a comprehensive investigation into parameter dependencies and propose a reasonable range for the hyperparameter τ. Our method offers a promising approach for optimizing molecular generation for enhanced protein binding, potentially increasing the efficiency of molecular discovery pipelines.

Deep Analogical Generative Design and Evaluation: Integration of Stable Diffusion and LoRA

Abstract The rapid evolution of generative design through artificial intelligence has opened new avenues for innovative product styling. Integrating this efficient generative technology with established professional theories presents a novel challenge in contemporary international design research. In response to this challenge, this article introduces a pioneering and collaborative approach for the swift generation of automobile styling designs. The primary objective is to investigate an intelligent generation method that incorporates analogical reasoning and Stable Diffusion to support industrial designers in innovating product styling. This study scrutinizes traditional analogical reasoning design alongside the intelligent analogical reasoning design proposed herein, elucidating the distinctions through multidimensional comparisons using illustrative examples. The proposed methodological framework encompasses several key steps. Initially, a dataset comprising branded automobile images is meticulously constructed. Subsequently, an exclusive style model is trained leveraging Stable Diffusion techniques, coupled with advanced computer graphics and machine learning methodologies. Following this, design requirements are inputted, facilitating intelligent analogical reasoning design across multiple spatial dimensions to yield diverse and innovative automobile styling solutions. Finally, eye-tracking experiments are conducted to quantitatively compare the traditional analogical reasoning design approach with the Stable Diffusion-based analogical reasoning design method. The results substantiate that the latter effectively generates innovative and diversified automobile design solutions. This research contributes to enhancing the quality of automobile styling design, optimizing the design efficiency of enterprises, and catalyzing innovation in the automobile styling design process.

Design, synthesis and biological assessment of novel pyridopyrimidine and chromenopyridopyrimidine analogues

The important pharmacological and medicinal facts concerning chromenopyridopyrimidine and pyridopyrimidine compounds motivated us to condense our efforts into constructing novel derivatives with valuable biological properties. A series of new chromenopyridopyrimidine 2–4 and different substituted pyridopyrimidine derivatives 6–9 and 13 were synthesized in one pot three components base-catalyzed reaction of aminothiouracil 1 with various aldehydes and different active methylene compounds. Also, substituted pyridopyrimidines 10 and 11 were synthesized when thiouracil 1 reacted only with cinnamaldehyde and acetyl acetone. The synthesized compounds’ structure was verified by microanalysis and spectral data, the biological studies demonstrated antibacterial and antifungal activities against E. coli, Pseudomonas aeruginosa, S. aureus, and C. albicans.

Compact grounded memristor model with resistorless and tunability features

This research article provides a circuit illustration of a grounded memristor emulator. An operational transconductance amplifier (OTA) is one of its active components, along with two transistors and one capacitor. With a simple flip of the input ports, the incremental and decremental settings for the proposed memristor may be preserved. With the capacity to function in the megahertz band, the circuit offers a resistorless and controllable feature. Using the Cadence Virtuoso EDA tool in an analog design environment (ADE), PSPICE simulation with 0.18 µm TSMC technology parameter has been used to illustrate the viability of the suggested memristor. It has been confirmed in the simulation section that the operating frequency and tunability responses in the current-voltage (I-V) plane are in reasonable agreement with the theory. The suggested memristor model’s resilience has also been tested using process corner, Monte Carlo analysis, and temperature analyses, as well as single and parallel connected structures. The suggested memristor model is simple and does not need additional sub-circuit components, making it appropriate for implementation in integrated circuits. The experimental demonstration has been carried out by making a prototype on a breadboard using ICs, which exhibits good agreement with theoretical and simulation results. Single/parallel combinations of memristor, chaotic oscillator, and high pass filter have been presented to demonstrate its application.

Gen-AI Methods, Molecular Docking and Molecular Dynamics Simulations for Identification of Novel Inhibitors of MmPL3 Transporter of Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb), the bacterium responsible for tuberculosis (TB), employs mycolic acids to build its cell wall. This robust structure plays a vital role in protecting the bacterium from external threats and contributes to its resistance against antibiotics. Mycobacterial membrane protein Large 3 (MmpL3), a secondary resistance nodulation division transporter, is essential in mycolic acid biosynthesis, transporting mycolic acid precursors into the periplasm using the proton motive force. Due to its role in bacterial cell wall formation, it is a promising target for new tuberculosis treatments. In this study, starting with 85 known MmPL3 compounds, the artificial intelligence (AI)-assisted tool “Design of Druglike Analogues (DeLA-Drug)” was employed to generate about 15,000 novel molecules. These compounds were then subjected to structure-based high-throughput virtual screening and molecular dynamics (MD) simulations to identify potential novel inhibitors of MmpL3. The binding affinity was obtained by docking the above molecules at the SQ109 binding site in MmPL3, followed by pharmacokinetics and toxicity, which were used to reduce the chemical space. Finally, five ligands were subjected to 100 ns MD simulations to investigate the binding energetics of inhibitors to MmpL3. These compounds demonstrated stable binding and favorable drug-like properties, indicating that they could serve as potential novel inhibitors of MmpL3 for Mtb.

Design, synthesis, biological and computational analysis of isatin-based bis-thiourea analogues as anti-diabetic and anti-nematode agents

A new series of isatin derivatives were synthesized, characterized by 1HNMR, 13CNMR and HREI-MS, and screened for α-glucosidase and alpha amylase inhibition. All the analogues were found to be dual inhibitors and showed good inhibitory potentials with IC50 values ranging from 5.28 ± 0.10 to 38.66 ± 0.30 µM (against alpha-amylase), and 5.45 ± 0.10 to 39.25 ± 0.50 µM (against alpha-glucosidase), as compared to the standard drug acarbose (IC50 = 11.12 ± 0.15 and 11.29 ± 0.07 µM, respectively). The most potent inhibitor among the series was analogue 24 (IC50 = 5.28 ± 0.10 for alpha-amylase and IC50 = 5.46 ± 0.10 µM for alpha-glucosidase), which has a nitro group attached at the meta-position of the phenyl ring A and the para position of phenyl ring B. Structure-activity relationship has been established mainly based on the position, nature and number of the substituent(s) attached to the phenyl ring. To investigate the binding interaction of the potent analog with the active site of an enzyme, molecular docking studies were carried out. To study the drug-likeness properties, the ADME study was also carried out. The most active compounds engage most of the amino acids composing the active site and display maximum interactions. These interactions majorly include formation of strong hydrogen bonds, which might be due to the presence of highly electronegative heteroatoms on aromatic rings. All the analogues were also tested for in vivo anti-nematodal activity against C. elegans to assess their cytotoxicity in comparison to the reference Levamisole. The cytotoxicity profile demonstrated that analogues 2, 7, 20 and 22 displayed minimum cytotoxicity at every concentration.

SWA: SoftWare for Analog Design Automation

We have developed SWA: SoftWare for Analog design automation. Its commands can describe analog and mixed-signal (AMS) layouts and schematics to replace the graphic editor with a program reflecting the knowledge of design experts. Also, it is able to utilize variables to parameterize schematics and layouts to fulfill design needs. We programmed a 10b 1 GS/s DAC using SWA with 8.3 K lines of code, which is about 1/10 compared with conventional programs. The programmed DAC is configurable with multiple voltages and multiple resolutions from 4 to 12 bits. The DAC schematic and layout generation with DRC and LVS SWA API can be finished in about 1 min.

Research and optimization of the gain characteristics of cascode amplifiers

Abstract. With the continuous development of IC technology, high-performance operational amplifiers are widely used in various circuit systems such as high-speed Analog-to-Digital Converters (ADCs), Digital-to-Analog Converters (DACs), switched capacitor filters, bandgap voltage reference sources, precision comparators. The amplifier has become the core unit circuit of analog IC and mixed-signal IC design, and its performance directly affects the overall performance of the circuit and system. The design of high-performance operational amplifiers has always been one of the hotspots in the research of analog IC design in order to meet the needs of various application fields. Cascode Amplifier is a classic structure widely used in high-frequency and high-gain electronic circuits. This project aims to study the gain characteristics of cascode amplifiers, explore the main factors affecting their gain, and propose methods to optimize the gain. Through theoretical analysis and simulation, the influence of different parameters on gain is verified. Then the optimization strategy is summarized to provide a reference for actual circuit design.

Machine learning-based rational design for efficient discovery of allatostatin analogs as promising lead candidates for novel IGRs.

Insect neuropeptide allatostatins (ASTs) play a vital role in regulating insect growth, development, and reproduction, making them potential candidates for new insect growth regulators (IGRs). However, the practical use of natural ASTs in pest management is constrained by their long sequences and high production costs, thus the development of AST analogs with shorter sequences and reduced cost is essential. Traditional methods for designing AST analogs are often time-consuming and resource-intensive. This study aims to employ new computational methodologies to understand the structure-activity relationship and efficiently discover potent AST analogs. Two machine learning models, utilizing multiple linear regression and support vector machine, were constructed to reveal the key structural factors that influence the juvenile hormone-inhibiting activity of AST analogs. These models suggested that a potent AST analog should contain styrene, hydrophilic, and aromatic groups, and rotatable bonds at positions 1, 2, 3, and 4, respectively. Six analogs (A52-A57) were designed and synthesized, and they exhibited potent juvenile hormone-inhibiting activity (IC50 < 16 nM). Notably, analog A53 showed the best activity (IC50 = 2.07 nM), surpassing that of most natural Dippu-ASTs, making it a potential lead candidate for IGRs. These models promote the efficient design, screening, and prioritization of new or untested AST analogs. The study clarifies how a machine learning-based strategy facilitates the development of AST analogs as novel IGR lead candidates, offering a useful reference for pest management. © 2024 Society of Chemical Industry.

Diffusion-based generative drug-like molecular editing with chemical natural language

Recently, diffusion models have emerged as a promising paradigm for molecular design and optimization. However, most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geometries, with limited research on molecular sequence diffusion models. The International Union of Pure and Applied Chemistry (IUPAC) names are more akin to chemical natural language than the Simplified Molecular Input Line Entry System (SMILES) for organic compounds. In this work, we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language (SMILES) and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language. We propose DiffIUPAC, a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings. Evaluation results demonstrate that our model outperforms existing methods and successfully captures the semantic rules of both chemical languages. Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints. Additionally, to illustrate the model's applicability in drug design, we conducted case studies in functional group editing, analogue design and linker design.

Design, synthesis, and evaluation of some benzimidazole analogs as AMPK agonists

AMP-activated protein kinase is a crucial regulator of cellular metabolism with potential therapeutic implications in various diseases. In this study, the benzimidazole scaffold was chosen for investigation with EX-229 as the lead compound. Modifications were made to the terminal carboxyl group, and the methylindole moiety was replaced with phenylpyrrolidinone to synthesize and assess the AMP-activated protein kinase-activating properties of 13 benzimidazole derivatives. In vitro studies have shown that most of the synthesized compounds have significant excitatory effects on AMP-activated protein kinase. Particularly, A11 demonstrated the most potent activity with an EC50 value of 39 nM. Combining activity data and molecular docking analysis revealed the necessity of end-molecule hydrogen bond donors for LYS29, and the importance of an appropriate torsion angle at the pyrrolidinone position to facilitate the formation of a crucial hydrogen bond with LYS31. These discoveries offer preliminary insights into the interaction of small-molecule drugs with the AMP-activated protein kinase protein and establish a foundation for the future development of AMP-activated protein kinase activators guided by structure–activity relationships.

Allosteric Inhibition of the β2-Adrenergic Receptor: Design and Synthesis of Cmpd-15 Analogs.

We designed and synthesized 27 new amide and dipeptide derivatives containing a substituted phenylalanine as negative allosteric modulators (NAMs) for the beta-2 adrenergic receptor (β2AR). These analogs aimed to improve the activity of our lead compound, Cmpd-15, by introducing variations in three key regions: the meta-bromobenzyl methylbenzamide (S1), para-formamidophenylalanine (S2), and 1-cyclohexyl-1-phenylacetyl (S3) groups. The synthesis involved the Pd-catalyzed β-C(sp3)-H arylation of N-acetylglycine with 1-iodo-4-substituent-benzenes as the key step. GloSensor cAMP accumulation assay revealed that six analogs (A1, C5, C6, C13, C15 and C17) surpass Cmpd-15 in β2AR allosteric function. This highlights the crucial role of the S1 region (meta-bromobenzyl methylbenzamide) in β2AR allostery while suggesting potential replaceability of the S2 region (para-formamidophenylalanine). These findings serve as a valuable springboard for further optimizing Cmpd-15, potentially leading to smaller, more active, and more stable β2AR-targeting NAMs.

Design, synthesis and antifungal activity of arylhydrazine analogs containing diphenyl ether fragments.

Succinate dehydrogenase (SDH) represents a critical target in the development of novel fungicides. To address the growing issue of resistance and safeguard the economic viability of agricultural production, the pursuit of new succinate dehydrogenase inhibitors (SDHIs) has emerged as a significant focus of contemporary research. In this project, 32 arylhydrazine derivatives containing diphenyl ether structural units were synthesized and evaluated for their fungicidal activities against Rhizoctonia solani, Sclerotinia sclerotiorum, Alternaria alternata, Gibberella zeae, Alternaria solani and Colletotrichum gloeosporioides. In an in vitro fungicidal activity assay, compound D6 showed significant inhibitory activity against R. solani with a half-maximum effective concentration (EC50) of 0.09 mg L-1. The in vivo fungicidal activity demonstrated that compound D6 inhibited R. solani by 95.39% in rice leaves, which was significantly better than that of boscalid (85.76%). The results of SDH enzyme assay, molecular docking simulation, mitochondrial membrane potential assay, cytoplasmic release studies and morphological observations demonstrated that the target compound D6 not only had significant SDH inhibitory activity, but also affected the membrane integrity of mycelium. Bioactivity screening and validation of the mechanism of action indicated that compound D6 was a potentially unique SDHI, acting on SDH while also affecting cell membrane permeability, which deserved further study. © 2024 Society of Chemical Industry.

Rationale design and synthesis of new roflumilast analogues as preferential selective and potent PDE-4B inhibitors

In this study, we designed and synthesized novel analogues of roflumilast that exhibit selective inhibition of PDE-4B. To accomplish this target; synthesis of novel series (4a-u, 5a-i, and 6) was done, aiming at obtaining new PDE-4B inhibitors hits based on the proposed pharmacophore, 1-(cyclopropylmethoxy)-2-(difluoromethoxy) benzene moiety. Enzyme assay was used to measure the IC50 values for the PDE-4B inhibition of all the synthesized compounds along with roflumilast as a reference drug. The results demonstrated that most of the examined candidates exhibited considerable inhibitory activity against the PDE-4B enzyme. The four compounds (4i, 4k, 4p, and 4q) exhibited the highest potency (IC50 = 7.25, 7.15, 5.50, 7.19 nM, respectively) with no significant inhibition difference from roflumilast (no statistical difference at p < 0.05). Interestingly, compound 4p with 3-OH and 4-OCH3 substituents was found to be the most potent against PDE-4B enzyme (IC50 = 5.50 nM), compared to that of roflumilast (IC50 = 2.36 nM). Moreover, the most potent derivatives 4i, 4k, 4p, and 4q were further tested for PDE-4D inhibitory activity to investigate their PDE-4D/PDE-4B selectivity ratio. Compound 4k showed the highest selectivity towards PDE-4B isozyme more than the reference drug roflumilast (PDE-4D/4B IC50 ratio for compound 4k and roflumilast = 3.22 and 3.02, respectively). Additionally, compound 4p was chosen to test its selectivity for PDE-4B over PDE-8A, PDE-11A, and PDE-1B compared to thereference drug roflumilast. Compound 4p showed approximately 6-fold selectivity for PDE-4B over PDE-8A, about 5-fold selectivity for PDE-4B over PDE-11A, and about 11-fold selectivity of PDE-4B over PDE-1B. Compound 4p showed a higher selectivity towards PDE-4B than PDE-1B, more than the reference compound roflumilast. Furthermore, the most potent compounds (4i, 4k, 4p, 4q) were subjected to further investigation, and their effects on the cAMP level and percentage of inhibition of tumor necrosis factor-alpha (TNF-α) were studied and compared with reference drug roflumilast. Compound 4q showed the highest increase in the level of intracellular cAMP (6.55 ± 0.37 pmol/mL) and compound 4i showed the highest % of TNF-α inhibition (77.22 %). On the other side, a molecular docking study against PDE-4B clarified that all the examined candidates achieved nearly similar binding modes with similar orientations to that of the native roflumilast ligand and showed higher docking scores than roflumilast.

Theoretical study of ultrafast spin dynamics in a Co-Fe molecular Prussian blue analog and spin logic functionality design

Theoretical study of ultrafast spin dynamics in a Co-Fe molecular Prussian blue analog and spin logic functionality design

Optimization of Advanced Support Parameters of Surrounding Rock in Tunnels Constructed by ADECO-RS

So as to efficiently address the distortion of surrounding rock in tunnels constructed utilizing ADECO-RS, it is crucial to define suitable parameters for advanced support systems. This study took the 8 # tunnel in the F3 portion of the E60 Expressway in Georgia as an engineering case. Initially, the original support scheme underwent systematic monitoring and analysis in the field. Subsequently, the FLAC3D 6.0 software was employed to examine the influence of the advanced pipe roof and tunnel face fiberglass bolts on the steady state of the surrounding rock. Optimization of the support parameters was also proposed. Further, the sensitivity of different parameters to the distortion of the rock surrounding the tunnel was analyzed and ranked via an orthogonal experiment. Ultimately, the effectiveness of the optimization scheme was evaluated by numerical methods and field observations. The findings of the research indicate the following: (1) The monitoring results of the original support parameters show that the irrational design of the support parameters can bring about deformation non-convergence in the tunnel’s surrounding rock. Support parameters must be optimized. (2) The spacing of the pipe roof is positively correlated with the distortion of the surrounding rock. In contrast, the length and the grouting strength are negatively correlated with the distortion of the surrounding rock. The reinforcement density, length, and lap length of glass fiber bolts exhibit an inverse relationship with the distortion of the surrounding rock. (3) The efficacy of pipe shed grouting in mitigating subsidence and deformation of the vault is superior, followed by the spacing of the supports. In contrast, the length of the supports demonstrates comparatively lesser effectiveness. Under optimal parameters, the vault subsidence was reduced by 23.2%, 10.2%, and 2.0%, respectively. The most significant factor controlling the extrusion deformation of the tunnel face is bolt lap length, followed by reinforcement density and then reinforcement length. Extrusion displacement was reduced by 52.5%, 40.3%, and 9.3%, respectively, under the optimal parameters. (4) In comparison to the primordial support system, the optimized support scheme reduces the subsidence of the vault by about one time and the convergence deformation around the cave by about two times. The research findings offer guidance for analogous engineering support design and parameter optimization.

Design and Synthesis of Mycophenolic Acid Analogues for Osteosarcoma Cancer Treatment

Design and Synthesis of Mycophenolic Acid Analogues for Osteosarcoma Cancer Treatment