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The biomineral haemozoin, or its synthetic analogue β-haematin (βH), has been the focus of several target-based screens for activity against Plasmodium falciparum parasites. Together with the known βH crystal structure, the availability of this screening data makes the target amenable to both structure-based and ligand-based virtual screening. In this study, molecular docking and machine learning techniques, including Bayesian and support vector machine classifiers, were used in sequence to screen the in silico ChemDiv 300k Representative Compounds library for inhibitors of βH with retained activity against P. falciparum . We commercially obtained and tested a prioritised set of inhibitors and identified the coumarin and iminodipyridinopyrimidine chemotypes as potent in vitro inhibitors of βH and whole cell parasite growth.

Cyclin-dependent kinases (CDKs) play essential roles in regulating the cell cycle and are among the most critical targets for cancer therapy and drug discovery. The primary objective of this research is to derive general structure–activity relationship (SAR) patterns for modeling the selectivity and activity levels of CDK inhibitors using machine learning methods. To accomplish this, 8592 small molecules with different binding affinities to CDK1, CDK2, CDK4, CDK5, and CDK9 were collected from Binding DB, and a diverse set of descriptors was calculated for each molecule. The supervised Kohonen networks (SKN) and counter propagation artificial neural networks (CPANN) models were trained to predict the activity levels and therapeutic targets of the molecules. The validity of models was confirmed through tenfold cross-validation and external test sets. Using selected sets of molecular descriptors (e.g. hydrophilicity and total polar surface area) we derived activity and selectivity maps to elucidate local regions in chemical space for active and selective CDK inhibitors. The SKN models exhibited prediction accuracies ranging from 0.75 to 0.94 for the external test sets. The developed multivariate classifiers were used for ligand-based virtual screening of 2 million random molecules of the PubChem database, yielding areas under the receiver operating characteristic curves ranging from 0.72 to 1.00 for the SKN model. Considering the persistent challenge of achieving CDK selectivity, this research significantly contributes to addressing the issue and underscores the paramount importance of developing drugs with minimized side effects.

Research on direct targets of traditional Chinese medicine (TCM) is the key to study the mechanism and material basis of it, but there is still no effective methods at present. We took Compound Danshen dropping pills (CDDP) as a study case to establish a strategy to identify significant direct targets of TCM. As a result, thirty potential active kinase targets of CDDP were identified. Nine of them had potential dose-dependent effects. In addition, the direct inhibitory effect of CDDP on three kinases, AURKB, MET and PIM1 were observed both on biochemical level and cellular level, which could not only shed light on the mechanisms of action involved in CDDP, but also suggesting the potency of drug repositioning of CDDP. Our results indicated that the research strategy including both in silico models and experimental validation that we built, were relatively efficient and reliable for direct targets identification for TCM prescription, which will help elucidating the mechanisms of TCM and promoting the modernization of TCM.

Bacterial resistance poses a significant public health challenge, particularly for pathogens prioritized by the World Health Organization, such as carbapenem-resistant Escherichia coli . There has been growing interest in exploring animal toxins as potential alternatives to antibiotics. This study centers on the rational design of an antibiotic peptide based on the sequence 115–129 from Myotoxin II, sourced from the venom of the snake Bothrops asper. We modified the original sequence 20 times using molecular docking and found that peptide sequence 20 (KHWYKHYRH) exhibited the highest affinity energy of − 7.6 kcal/mol for lipopolysaccharide (LPS). The in vitro potency was assessed against E. coli , with an IC 50 of 0.27 mg/mL, while P. aeruginosa (ATCC 27853) showed an IC 50 of 2.93 mg/mL. Conversely, the peptide was ineffective against resistant strains, such as the NDM-1-positive Klebsiella pneumoniae (ATCC BAA-2146) and the ESBL clinical isolate E. coli (CTX-M). Additionally, the safety of peptide 20 was evaluated, revealing that none of the tested concentrations caused hemolytic activity or loss of cellular viability in L929 and Caco-2 cells. This indicates that rational, structure-based design is an effective strategy for developing safe peptides.

Accurate estimation of protein–ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R 2  = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity predictions in minutes, making it suitable for practical applications in hit identification or lead optimization. The paper presents the universal QM-based scoring function that accurately and rapidly predicts protein-ligand binding affinities, outperforming current computational tools. This is demonstrated on the PL-REX experimental benchmark dataset.

In our search for novel small molecules activating procaspase-3, we have designed and synthesized two series of novel ( E )- N' -arylidene-2-(2-oxoindolin-1-yl)acetohydrazides ( 4) and (Z)- 2-(5-substituted-2-oxoindolin-1-yl)- N' -(2-oxoindolin-3-ylidene)acetohydrazides ( 5) . Cytotoxic evaluation revealed that the compounds showed notable cytotoxicity toward three human cancer cell lines: colon cancer SW620, prostate cancer PC-3, and lung cancer NCI-H23. Especially, six compounds, including 4f–h and 4n–p , exhibited cytotoxicity equal or superior to positive control PAC-1, the first procaspase-3 activating compound. The most potent compound 4o was three- to five-fold more cytotoxic than PAC-1 in three cancer cell lines tested. Analysis of compounds effects on cell cycle and apoptosis demonstrated that the representative compounds 4f, 4h, 4n, 4o and 4p (especially 4o ) accumulated U937 cells in S phase and substantially induced late cellular apoptosis. The results show that compound 4o would serve as a template for further design and development of novel anticancer agents.

Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds -- including efficacy, pharmacokinetics and safety -- need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.

Integrins are cell adhesion and signalling proteins crucial to a wide range of biological functions. Effective marketed treatments have successfully targeted integrins αIIbβ3, α4β7/α4β1 and αLβ2 for cardiovascular diseases, inflammatory bowel disease/multiple sclerosis and dry eye disease, respectively. Yet, clinical development of others, notably within the RGD-binding subfamily of αv integrins, including αvβ3, have faced significant challenges in the fields of cancer, ophthalmology and osteoporosis. New inhibitors of the related integrins αvβ6 and αvβ1 have recently come to the fore and are being investigated clinically for the treatment of fibrotic diseases, including idiopathic pulmonary fibrosis and nonalcoholic steatohepatitis. The design of integrin drugs may now be at a turning point, with opportunities to learn from previous clinical trials, to explore new modalities and to incorporate new findings in pharmacological and structural biology. This Review intertwines research from biological, clinical and medicinal chemistry disciplines to discuss historical and current RGD-binding integrin drug discovery, with an emphasis on small-molecule inhibitors of the αv integrins.Integrins are key signalling molecules that are present on the surface of subsets of cells and are therefore good potential therapeutic targets. In this Review, Hatley and colleagues discuss the development of integrin inhibitors, particularly the challenges in developing inhibitors for integrins that contain an αv-subunit, and suggest how these challenges could be addressed.

A series of novel diphenylamine fluorinated chalcone derivatives ( B1 – B10 ) were synthesized and characterized using 1 H and 13 C NMR, IR, and MS, and purity was determined using HPLC. The compounds were evaluated for their antimicrobial, antimalarial, and anticancer activities, with Chloramphenicol, Griseofulvin, and 5-Fluorouracil serving as standard reference drugs. Notably, B6 exhibited excellent antifungal activity, comparable to that of the standard drug Griseofulvin. Compounds B3 and B5 showed strong antimalarial effects against Plasmodium falciparum . Both B3 and B5 exhibit substantial cytotoxicity against HeLa cells, with IC 50 values of 24.53 µg/ml for B5 and 32.42 µg/ml for B3 . These results clearly demonstrate that both compounds outperform the standard drug 5-Fluorouracil, establishing their strong potential as effective alternatives in cancer therapy. Molecular docking studies revealed that B3 and B6 effectively interacted with the active site of Falcilysin , while B5 and B7 showed favourable binding to proteins 6GUE and 2 × 7 F . Molecular dynamics simulations confirmed the stability of B3 and B6 with P. falciparum , while B5 and B3 exhibited promising interactions with 6GUE and 2X7F . These results suggest that compounds B3 and B5 are potential lead candidates for developing novel antimicrobial, antimalarial, and anticancer therapies.

PROTAC is a drug development technology that uses the Ubiquitin-Proteasome System (UPS) to degrade target proteins, and enhances the degradation ability of target proteins through E3 ubiquitin ligase, which can further enhance the anti-tumor effect of targeted drug molecules. In this study, a series of dual-target MDM2/MDMX stapled peptide PROTAC based on SM3-4 were designed and synthesized, and the stapled peptide PROTAC DSM3-2 and DSM3-5 screened in the study inhibited tumor cell growth in vitro at low µM concentrations. The results showed that the enhancement of stapled peptide activity was positively correlated with the increase of helicity, which provided an effective research basis for the dual-target anti-tumor stapled peptide PROTAC. Molecular docking experiments have shown that the binding peptide DSM3-2 can effectively bind to the target proteins MDM2 and MDMX to exert a dual targeting effect on tumor cells.