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Background: Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. ARTICLE HISTORY Objective: This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field. Received: June 07, 2024 Revised: August 11, 2024 Accepted: August 19, 2024 Methods: We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literaDOI: 10.2174/0113894501330963240905083020 ture. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. Results: The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. Conclusion: The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process. Show less

PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both in vitro and in vivo studies. PandaOmics is a core component of Insilico Medicine's Pharma.ai drug discovery suite, which also includes Chemistry42 for the de novo generation of novel small molecules, and inClinico─a data-driven multimodal platform that forecasts a clinical trial's probability of successful transition from phase 2 to phase 3. In this paper, we demonstrate how the PandaOmics platform can efficiently identify novel molecular targets and biomarkers for various diseases. Show less

Parthanatos is a cell death signaling pathway that has emerged as a compelling target for pharmaceutical intervention. It plays a pivotal role in the neuron loss and neuroinflammation that occurs in Parkinson's Disease (PD), Alzheimer's Disease (AD), Huntington's Disease (HD), Amyotrophic Lateral Sclerosis (ALS), and stroke. There are currently no treatments available to humans to prevent cell death in any of these diseases. This review provides an in-depth examination of the current understanding of the Parthanatos mechanism, with a particular focus on its implications in neuroinflammation and various diseases discussed herein. Furthermore, we thoroughly review potential intervention targets within the Parthanatos pathway. We dissect recent progress in inhibitory strategies, complimented by a detailed structural analysis of key Parthanatos executioners, PARP-1, AIF, and MIF, along with an assessment of their established inhibitors. We hope to introduce a new perspective on the feasibility of targeting components within the Parthanatos pathway, emphasizing its potential to bring about transformative outcomes in therapeutic interventions. By delineating therapeutic opportunities and known targets, we seek to emphasize the imperative of blocking Parthanatos as a precursor to developing disease-modifying treatments. This comprehensive exploration aims to catalyze a paradigm shift in our understanding of potential neurodegenerative disease therapeutics, advocating for the pursuit of effective interventions centered around Parthanatos inhibition. Show less

ChEMBL (https://www.ebi.ac.uk/chembl/) is a manually curated, high-quality, large-scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like properties, previously described in the 2012, 2014, 2017 and 2019 Nucleic Acids Research Database Issues. Since its introduction in 2009, ChEMBL's content has changed dramatically in size and diversity of data types. Through incorporation of multiple new datasets from depositors since the 2019 update, ChEMBL now contains slightly more bioactivity data from deposited data vs data extracted from literature. In collaboration with the EUbOPEN consortium, chemical probe data is now regularly deposited into ChEMBL. Release 27 made curated data available for compounds screened for potential anti-SARS-CoV-2 activity from several large-scale drug repurposing screens. In addition, new patent bioactivity data have been added to the latest ChEMBL releases, and various new features have been incorporated, including a Natural Product likeness score, updated flags for Natural Products, a new flag for Chemical Probes, and the initial annotation of the action type for ∼270 000 bioactivity measurements. Show less

Researchers are using new molecules, engineered immune cells and gene therapy to kill senescent cells and treat age-related diseases. Researchers are using new molecules, engineered immune cells and gene therapy to kill senescent cells and treat age-related diseases. Show less

Programmed cell death (PCD) is a fundamental biological process for maintaining cellular equilibrium and regulating development, health, and disease across all living organisms. Among the various types of PCD, apoptosis plays a pivotal role in numerous diseases, notably cancer. Cancer cells frequently develop mechanisms to evade apoptosis, increasing resistance to standard chemotherapy treatments. This resistance has prompted extensive research into alternative mechanisms of programmed cell death. One such pathway is oncosis, characterized by significant energy consumption, cell swelling, dilation of the endoplasmic reticulum, mitochondrial swelling, and nuclear chromatin aggregation. Recent research suggests that oncosis can impact conditions such as chemotherapeutic cardiotoxicity, myocardial ischemic injury, stroke, and cancer, mediated by specific oncosis-related proteins. In this review, we provide a detailed examination of the morphological and molecular features of oncosis and discuss various natural or small molecule compounds that can induce this type of cell death. Additionally, we summarize the current understanding of the molecular mechanisms underlying oncosis and its role in both normal physiology and pathological conditions. These insights aim to illuminate future research directions and propose innovative strategies for leveraging oncosis as a therapeutic tool against human diseases and cancer resistance. Show less

The Keap1-Nrf2 signaling pathway plays a crucial role in cellular defense against oxidative stress-induced damage. Its activation entails the expression and transcriptional regulation of several proteins involved in detoxification and antioxidation processes within the organism. Keap1, serving as a pivotal transcriptional regulator within this pathway, exerts control over the activity of Nrf2. Various post-translational modifications (PTMs) of Keap1, such as alkylation, glycosylation, glutathiylation, S-sulfhydration, and other modifications, impact the binding affinity between Keap1 and Nrf2. Consequently, this leads to the accumulation of Nrf2 and its translocation to the nucleus, and subsequent activation of downstream antioxidant genes. Given the association between the Keap1-Nrf2 signaling pathway and various diseases such as cancer, neurodegenerative disorders, and diabetes, comprehending the post-translational modification of Keap1 not only deepens our understanding of Nrf2 signaling regulation but also contributes to the identification of novel drug targets and biomarkers. Consequently, this knowledge holds immense importance in the prevention and treatment of diseases induced by oxidative stress. Show less

Organizing and partitioning sets of chemical structures is of considerable practical significance, e.g., in compound library analysis and the postprocessing of screening hit lists. Approaches such as unsupervised clustering are computationally demanding and dataset-dependent; on the other hand, rule-based methods, such as those based on Murcko scaffolds, have linear time complexity but are often too fine-grained, leading to a large number of singletons or sparsely populated classes. An alternative rule-based method that seeks to achieve an optimal balance when grouping compounds into sets is the 'Scaffold Identification and Naming System' (SCINS). To facilitate public use of this previously published method, here, we provide an open-source Python implementation of SCINS, dependent only on RDKit. We show that SCINS can be useful in identifying sparsely and densely populated regions in chemical space in large databases, here exemplified with Enamine REAL Diverse and ChEMBL. We find that Enamine REAL Diverse covers a much smaller SCINS space relative to ChEMBL, whereas the opposite is true when Murcko and generic Murcko scaffolds are considered. Additionally, we show that SCINS can result in chemically intuitive grouping of medium-sized sets of bioactive compounds, which can be useful in compound selection from virtual screening campaigns as well as postprocessing of experimental hit lists. Hence, in this work, we provide both an open-source implementation of SCINS and its characterization with relevant use cases. Show less

Motivation: Drug–target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could Show less

NRF2 (nuclear factor erythroid 2-related factor 2) is a master regulator of protective responses in healthy tissues. However, when it is active in tumor cells, it can result in drug resistance. KEAP1, the endogenous NRF2 inhibitor, binds NRF2 and redirects it to proteasomal degradation, so the KEAP1/NRF2 interaction is critical for maintaining NRF2 at a basal level. A number of clinically relevant KEAP1 mutations were shown to disrupt this critical KEAP1/NRF2 interaction, leading to elevated NRF2 levels and drug resistance. Here, we describe a small-molecule NRF2 inhibitor, R16, that selectively binds KEAP1 mutants and restores their NRF2-inhibitory function by repairing the disrupted KEAP1/NRF2 interactions. R16 substantially sensitizes KEAP1-mutated tumor cells to cisplatin and gefitinib, but does not do so for wild-type KEAP1 cells, and sensitizes KEAP1 G333C-mutated xenograft to cisplatin. We developed a BRET2-based biosensor system to detect the KEAP1/NRF2 interaction and classify KEAP1 mutations. This strategy would identify drug-resistant KEAP1 somatic mutations in clinical molecular profiling of tumors. Show less

During recent years, accumulating evidence suggested that metal-based candidate drugs are promising modulators of cytoskeletal and cytoskeleton-associated proteins. This was substantiated by the identification and validation of actin, vimentin and plectin as targets of distinct ruthenium(II)- and platinum(II)-based modulators. Despite this, structural information about molecular interaction is scarcely available. Here, we compile the scattered reports about metal-based candidate molecules that influence the cytoskeleton, its associated proteins and explore their potential to interfere in cancer-related processes, including proliferation, invasion and the epithelial-to-mesenchymal transition. Advances in this field depend crucially on determining binding sites and on gaining comprehensive insight into molecular drug-target interactions. These are key steps towards establishing yet elusive structure-activity relationships. Show less

Transition metal elements, such as copper, play diverse and pivotal roles in oncology. They act as constituents of metalloenzymes involved in cellular metabolism, function as signaling molecules to regulate the proliferation and metastasis of tumors, and are integral components of metal-based anticancer drugs. Notably, recent research reveals that excessive copper can also modulate the occurrence of programmed cell death (PCD), known as cuprotosis, in cancer cells. This modulation occurs through the disruption of tumor cell metabolism and the induction of proteotoxic stress. This discovery uncovers a mode of interaction between transition metals and proteins, emphasizing the intricate link between copper homeostasis and tumor metabolism. Moreover, they provide innovative therapeutic strategies for the precise diagnosis and treatment of malignant tumors. At the crossroads of chemistry and oncology, we undertake a comprehensive review of copper homeostasis in tumors, elucidating the molecular mechanisms underpinning cuproptosis. Additionally, we summarize current nanotherapeutic approaches that target cuproptosis and provide an overview of the available laboratory and clinical methods for monitoring this process. In the context of emerging concepts, challenges, and opportunities, we emphasize the significant potential of nanotechnology in the advancement of this field. Show less

Apoptosis is a form of regulated cell death (RCD) that involves proteases of the caspase family. Pharmacological and genetic strategies that experimentally inhibit or delay apoptosis in mammalian systems have elucidated the key contribution of this process not only to (post-)embryonic development and adult tissue homeostasis, but also to the etiology of multiple human disorders. Consistent with this notion, while defects in the molecular machinery for apoptotic cell death impair organismal development and promote oncogenesis, the unwarranted activation of apoptosis promotes cell loss and tissue damage in the context of various neurological, cardiovascular, renal, hepatic, infectious, neoplastic and inflammatory conditions. Here, the Nomenclature Committee on Cell Death (NCCD) gathered to critically summarize an abundant pre-clinical literature mechanistically linking the core apoptotic apparatus to organismal homeostasis in the context of disease. Show less

Ferroptosis is evolving as a highly promising approach to combat difficult-to-treat tumour entities including therapy-refractory and dedifferentiating cancers^1-3. Recently, ferroptosis suppressor protein-1 (FSP1), along with extramitochondrial ubiquinone or exogenous vitamin K and NAD(P)H/H⁺ as an electron donor, has been identified as the second ferroptosis-suppressing system, which efficiently prevents lipid peroxidation independently of the cyst(e)ine-glutathione (GSH)-glutathione peroxidase 4 (GPX4) axis^4-6. To develop FSP1 inhibitors as next-generation therapeutic ferroptosis inducers, here we performed a small molecule library screen and identified the compound class of 3-phenylquinazolinones (represented by icFSP1) as potent FSP1 inhibitors. We show that icFSP1, unlike iFSP1, the first described on-target FSP1 inhibitor⁵, does not competitively inhibit FSP1 enzyme activity, but instead triggers subcellular relocalization of FSP1 from the membrane and FSP1 condensation before ferroptosis induction, in synergism with GPX4 inhibition. icFSP1-induced FSP1 condensates show droplet-like properties consistent with phase separation, an emerging and widespread mechanism to modulate biological activity⁷. N-terminal myristoylation, distinct amino acid residues and intrinsically disordered, low-complexity regions in FSP1 were identified to be essential for FSP1-dependent phase separation in cells and in vitro. We further demonstrate that icFSP1 impairs tumour growth and induces FSP1 condensates in tumours in vivo. Hence, our results suggest that icFSP1 exhibits a unique mechanism of action and synergizes with ferroptosis-inducing agents to potentiate the ferroptotic cell death response, thus providing a rationale for targeting FSP1-dependent phase separation as an efficient anti-cancer therapy. Show less

Non-coding RNAs (ncRNAs) are, arguably, the enigma of the RNA transcriptome. Even though there are more annotated ncRNAs (25,967) compared to mRNAs (19,827), we know far less about each of the genes that produce ncRNA, especially in terms of their regulation, molecular functions, and interactions. Further, we are only beginning to understand the role of differential regulation or function of ncRNAs caused by genetic and epigenetic perturbations, such as single nucleotide variants (SNV), deletions, insertions, and histone/DNA modifications. The 22 papers in this Special Issue describe the emerging roles of ncRNAs in neurological, cardiovascular, immune, and hepatic systems, to name a few, as well as in diseases such as cancer, Prader-Willi Syndrome, cardiac arrhythmias, and diabetes. As we begin to understand the function and regulation of this class of RNAs, strategies targeting ncRNAs could lead to improved therapeutic interventions for some conditions. Show less

Cells tend to disintegrate themselves or are forced to undergo such destructive processes in critical circumstances. This complex cellular function necessitates various mechanisms and molecular pathways in order to be executed. The very nature of cell death is essentially important and vital for maintaining homeostasis, thus any type of disturbing occurrence might lead to different sorts of diseases and dysfunctions. Cell death has various modalities and yet, every now and then, a new type of this elegant procedure gets to be discovered. The diversity of cell death compels the need for a universal organizing system in order to facilitate further studies, therapeutic strategies and the invention of new methods of research. Considering all that, we attempted to review most of the known cell death mechanisms and sort them all into one arranging system that operates under a simple but subtle decision-making (If \ Else) order as a sorting algorithm, in which it decides to place and sort an input data (a type of cell death) into its proper set, then a subset and finally a group of cell death. By proposing this algorithm, the authors hope it may solve the problems regarding newer and/or undiscovered types of cell death and facilitate research and therapeutic applications of cell death. Show less

Targeting of G-quadruplex (G-Q) nucleic acids, which are helical four-stranded structures formed from guanine-rich nucleic acid sequences, has emerged in recent years as an appealing opportunity for drug intervention in anticancer therapy. Small-molecule drugs can stabilize quadruplex structures, promoting selective downregulation of gene expression and telomerase inhibition and also activating DNA damage responses. Thus, rational design of small molecular ligands able to selectively interact with and stabilize G-Q structures is a promising strategy for developing potent anti-cancer drugs with selective toxicity towards cancer cells over normal ones. Here, the outcomes of a thorough computational investigation of a recently synthesized monofunctional Pt^II complex (Pt1), whose selectivity for G-Q is activated by what is called adaptive binding, are reported. Quantum mechanics and molecular dynamics calculations have been employed for studying the classical key steps of the mechanism of action of Pt^II complexes, the conversion of the non-charged and non-planar Pt1 complex into a planar and charged Pt^II (Pt2) complex able to play the role of a G-Q binder and, finally, the interaction of Pt2 with G-Q. The information obtained from such an investigation allows us to rationalize the behavior of the novel Pt^II complex proposed to be activated by adaptive binding toward selective interaction with G-Q or similar molecules and can be exploited for designing ligands with more effective recognition ability toward G-quadruplex DNA. Show less

Targeting of G-quadruplex (G-Q) nucleic acids, which are helical four-stranded structures formed from guanine-rich nucleic acid sequences, has emerged in recent years as an appealing opportunity for drug intervention in anticancer therapy. Small-molecule drugs can stabilize quadruplex structures, promoting selective downregulation of gene expression and telomerase inhibition and also activating DNA damage responses. Thus, rational design of small molecular ligands able to selectively interact with and stabilize G-Q structures is a promising strategy for developing potent anti-cancer drugs with selective toxicity towards cancer cells over normal ones. Here, the outcomes of a thorough computational investigation of a recently synthesized monofunctional Pt^II complex (Pt1), whose selectivity for G-Q is activated by what is called adaptive binding, are reported. Quantum mechanics and molecular dynamics calculations have been employed for studying the classical key steps of the mechanism of action of Pt^II complexes, the conversion of the non-charged and non-planar Pt1 complex into a planar and charged Pt^II (Pt2) complex able to play the role of a G-Q binder and, finally, the interaction of Pt2 with G-Q. The information obtained from such an investigation allows us to rationalize the behavior of the novel Pt^II complex proposed to be activated by adaptive binding toward selective interaction with G-Q or similar molecules and can be exploited for designing ligands with more effective recognition ability toward G-quadruplex DNA. Show less

The therapeutic efficacy of cisplatin and oxaliplatin depends on the balance between the DNA damage induction and the DNA damage response of tumor cells. Based on clinical evidence, oxaliplatin is administered to cisplatin-unresponsive cancers, but the underlying molecular causes for this tumor specificity are not clear. Hence, stratification of patients based on DNA repair profiling is not sufficiently utilized for treatment selection. Using a combination of genetic, transcriptomics and imaging approaches, we identified factors that promote global genome nucleotide excision Show less

Motivation: Screening new drug–target interactions (DTIs) by traditional experimental methods is costly and time-consuming. Recent advances in knowledge graphs, chemical linear notations, and genomic data enable researchers to develop computational-based-DTI models, which play a pivotal role in drug repurposing and discovery. However, there still needs to develop a multimodal fusion DTI model that integrates available heterogeneous data into a unified framework. Results: We developed MDTips, a multimodal-data-based DTI prediction system, by fusing the knowledge graphs, gene expression profiles, and Show less

Chemistry42 is a software platform for de novo small molecule design and optimization that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methodologies. Chemistry42 efficiently generates novel molecular structures with optimized properties validated in both in vitro and in vivo studies and is available through licensing or collaboration. Chemistry42 is the core component of Insilico Medicine's Pharma.ai drug discovery suite. Pharma.ai also includes PandaOmics for target discovery and multiomics data analysis, and inClinico─a data-driven multimodal forecast of a clinical trial's probability of success (PoS). In this paper, we demonstrate how the platform can be used to efficiently find novel molecular structures against DDR1 and CDK20. Show less

ZBP1 senses viral Z-RNAs to induce necroptotic cell death to restrain viral infection. ZBP1 is also thought to recognize host cell-derived Z-RNAs to regulate organ development and tissue inflammation in mice. However, it remains unknown how the host-derived Z-RNAs are formed and how these endogenous Z-RNAs are sensed by ZBP1. Here, we report that oxidative stress strongly induces host cell endogenous Z-RNAs, and the Z-RNAs then localize to stress granules for direct sensing by ZBP1 to trigger necroptosis. Oxidative stress triggers dramatically increase Z-RNA levels in tumor cells, and the Z-RNAs then directly trigger tumor cell necroptosis through ZBP1. Localization of the induced Z-RNAs to stress granules is essential for ZBP1 sensing. Oxidative stress-induced Z-RNAs significantly promote tumor chemotherapy via ZBP1-driven necroptosis. Thus, our study identifies oxidative stress as a critical trigger for Z-RNA formation and demonstrates how Z-RNAs are directly sensed by ZBP1 to trigger anti-tumor necroptotic cell death. Show less

Adavosertib selectively inhibits Wee1, which regulates intra-S and G2/M cell-cycle checkpoints. This study investigated dosing schedules for adavosertib monotherapy, determining the maximum tolerated dose (MTD) and recommended Phase II dose (RP2D) in patients with advanced solid tumors.Patients received oral adavosertib qd or bid on a 5/9 schedule (5 days on treatment, 9 days off) in 14-day cycles, or qd on one of two 5/2 schedules (weekly, or for 2 of 3 weeks) in 21-day cycles. Safety, efficacy, and pharmacokinetic analyses were performed.Sixty-two patients (female, 64.5%; median age, 61.5 years; most common primary tumors: lung [24.2%], ovary [21.0%]) received treatment (qd schedules, n = 50; bid schedules, n = 12) for 1.8 months (median). Median time to maximum adavosertib concentration was 2.2-4.1 h; mean half-life was 5-12 h. Adverse events (AEs) caused dose reductions, interruptions and discontinuations in 17 (27.4%), 25 (40.3%) and 4 (6.5%) patients, respectively. Most common grade ≥ 3 AEs were anemia, neutropenia (each n = 9, 14.5%) and diarrhea (n = 8, 12.9%). Seven (11.3%) patients experienced 10 treatment-related serious AEs (pneumonia n = 2 [3.2%], dehydration n = 2 [3.2%], anemia n = 1 [1.6%], febrile neutropenia n = 1 [1.6%], and thrombocytopenia n = 1 [1.6%]). Overall objective response rate was 3.4% (2/58); disease control rate was 48.4% (30/62); median progression-free survival was 2.7 months.MTDs were 125 mg (bid 5/9) and 300 mg (qd 5/9 and 5/2 for 2 of 3 weeks); RP2D was 300 mg (qd 5/2 for 2 of 3 weeks). The safety profile was manageable, acceptable, and generally concordant with the known safety profile. Show less

Background

Targeting AKT suppresses tumor growth through inducing apoptosis, however, during which whether other forms of cell death occurring is poorly understood.

Methods

The effects of increasing PARP1 dependent cell death (parthanatos) induced by inhibiting AKT on cell proliferation were determined by CCK-8 assay, colony formation assay, Hoechst 33,258 staining and analysis of apoptotic cells by flow cytometry. For the detailed mechanisms during this process, Western blot analysis, qRT-PCR analysis, immunofluorescence and co-immunoprecipitation were performed. Moreover, the inhibition of tumor growth by inducing p53/SIRT6/PARP1-dependent parthanatos was further verified in the xenograft mouse model.

Results

For the first time, we identified that inhibiting AKT triggered parthanatos, a new form of regulated cell death, leading to colon cancer growth suppression. For the mechanism investigation, we found that after pharmacological or genetic AKT inhibition, p53 interacted with SIRT6 and PARP1 directly to activate it, and promoted the formation of PAR polymer. Subsequently, PAR polymer transported to outer membrane of mitochondria and resulted in AIF releasing and translocating to nucleus thus promoting cell death. While, blocking PARP1 activity significantly rescued colon cancer from death. Furthermore, p53 deletion or mutation eliminated PAR polymer formation, AIF translocation, and PARP1 dependent cell death, which was promoted by overexpression of SIRT6. Meanwhile, reactive oxygen species production was elevated after inhibition of AKT, which might also play a role in the occurrence of parthanatos. In addition, inhibiting AKT initiated protective autophagy simultaneously, which advanced tumor survival and growth.

Conclusion

Our findings demonstrated that AKT inhibition induced p53-SIRT6-PARP1 complex formation and the activation of parthanatos, which can be recognized as a novel potential therapeutic strategy for cancer. Video Abstract. Show less

Three tridentate Schiff base ligands were synthesized from the reactions between 2-picolylamine and salicylaldehyde derivatives (3-ethoxy (OEt), 4-diethylamino (NEt₂) and 4-hydroxy (OH)). Complexes with the general formula Pt(N^N^O)Cl were obtained from reactions between the ligands and K₂PtCl₄. The ligands and their complexes were characterized by NMR spectroscopy, mass spectrometry and elemental analysis. Further confirmation of the structure of Pt-OEt was achieved by single-crystal X-ray diffraction. The DMSO/chlorido exchange process at Pt-OEt was investigated by monitoring the change in conductivity, revealing very slow dissociation in DMSO. Moreover, solvent/chlorido exchange for Pt-OEt and Pt-NEt₂ were investigated by NMR spectroscopy in DMSO and DMSO/D₂O; Pt-NEt₂ forms an adduct with DMSO while Pt-OEt forms adducts with DMSO and water. The DNA-binding behaviour of the platinum(ii) complexes was investigated by two techniques. Pt-NEt₂ has the best apparent binding constant. The intercalation mode of interaction with ct-DNA was suggested by molecular docking studies and the increase in the relative viscosity of ct-DNA with increasing concentrations of the platinum(ii) complexes. However, the gradual decrease in the relative viscosity over time at constant concentration of platinum(ii) complexes indicated a shift from intercalation to a covalent binding mode. Anticancer activities of the ligands and their platinum(ii) complexes were examined against two cell lines. The platinum(ii) complexes exhibit superior cytotoxicity to that of their ligands. Among the platinum(ii) complexes, Pt-OEt possesses the best IC₅₀ against both cell lines, its cytotoxicity being comparable to that observed for cisplatin. Cell cycle arrest in the HepG2 cell line upon treatment with Pt-OEt and Pt-NEt₂ was investigated and compared to that of cisplatin; the change in the cell accumulation patterns supports the presumption of an apoptotic cell death pathway. The optimized structures of the B-DNA trimer adducts with the platinum complexes showed hydrogen-bonding interactions between the ligands and nucleobases, affecting the inter-strand hydrogen bonding within the DNA, and highlighting the strong ability of the complexes to induce conformational changes in the DNA, leading to the activation of apoptotic cell death. In summary, the current study demonstrates promising new anticancer platinum(ii) complexes with highly flexible tridentate ligands; the functional groups on the ligands are important in tuning their DNA binding/anticancer properties. Show less