Strategy For Discovery Research Articles

Accelerating PROTACs Discovery Through a Direct-to-Biology Platform Enabled by Modular Photoclick Chemistry.

Proteolysis targeting chimeras (PROTACs) have emerged as a promising strategy for drug discovery and exploring protein functions, offering a revolutionary therapeutic modality. Currently, the predominant approach to PROTACs discovery mainly relies on an empirical design-synthesis-evaluation process involving numerous cycles of labor-intensive synthesis-purification and bioassay data collection. Therefore, the development of innovative methods to expedite PROTAC synthesis and exploration of chemical space remains highly desired. Here, a direct-to-biology strategy is reported to streamline the synthesis of PROTAC libraries on plates, enabling the seamless transfer of reaction products to cell-based bioassays without the need for additional purification. By integrating amide coupling and light-induced primary amines and o-nitrobenzyl alcohols cyclization (PANAC) photoclick chemistry into a plate-based synthetic process, this strategy produces PROTAC libraries with high efficiency and structural diversity. Moreover, by employing this platform for PROTACs screening, wesmoothly found potent PROTACseffectively inhibit triple-negative breast cancer (TNBC) cell growth and induce rapid, selective targeted degradation of cyclin-dependent kinase 9 (CDK9). The study introduces a versatile platform for assembling PROTACs on plates, followed by direct biological evaluation. This approach provides a promising opportunity for high-throughput synthesis of PROTAC libraries, thereby enhancing the efficiency of exploring chemical space and accelerating the discovery of PROTACs.

A Machine Learning Approach for Early-Stage Autism Spectrum Disorders Detection

Abstract: The undertaking proposes a Machine Learning (ML) system for early ID of Autism Spectrum illness (ASD), recognizing the hardships of taking out the sickness yet endeavoring to diminish its seriousness through early therapies. The proposed system tests four Feature Scaling (FS) methods (Quantile Transformer, Power Transformer, Normalizer, Max Abs Scaler) on four normal ASD datasets from babies to grown-ups. ML calculations (e.g., Ada Boost, Random Forest, Decision Tree, K-Nearest Neighbors, Gaussian Naïve Bayes, Logistic Regression, SVM, LDA) are utilized on included scaled datasets. The best classifiers and FS strategies for each age bunch are recognized utilizing factual estimations. The voting classifier predicts ASD with the greatest accuracy for Babies, Kids, Young people, and Grown-ups. The task incorporates a definite element significance examination utilizing four Component Determination Strategies to underline the significance of calibrating ML techniques in foreseeing ASD across age gatherings and to assist medical services specialists with pursuing ASD screening choices. In contrast with current early ASD discovery strategies, the proposed structure performs well. To further develop ASD identification strength and precision, a group procedure using a Voting Classifier with Random Forest (RF) and AdaBoost achieved 100% accuracy.

Cheminformatics approach to identify andrographolide derivatives as dual inhibitors of methyltransferases (nsp14 and nsp16) of SARS-CoV-2

The Covid-19 pandemic outbreak has accelerated tremendous efforts to discover a therapeutic strategy that targets severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to control viral infection. Various viral proteins have been identified as potential drug targets, however, to date, no specific therapeutic cure is available against the SARS-CoV-2. To address this issue, the present work reports a systematic cheminformatic approach to identify the potent andrographolide derivatives that can target methyltransferases of SARS-CoV-2, i.e. nsp14 and nsp16 which are crucial for the replication of the virus and host immune evasion. A consensus of cheminformatics methodologies including virtual screening, molecular docking, ADMET profiling, molecular dynamics simulations, free-energy landscape analysis, molecular mechanics generalized born surface area (MM-GBSA), and density functional theory (DFT) was utilized. Our study reveals two new andrographolide derivatives (PubChem CID: 2734589 and 138968421) as natural bioactive molecules that can form stable complexes with both proteins via hydrophobic interactions, hydrogen bonds and electrostatic interactions. The toxicity analysis predicts class four toxicity for both compounds with LD50 value in the range of 500–700 mg/kg. MD simulation reveals the stable formation of the complex for both the compounds and their average trajectory values were found to be lower than the control inhibitor and protein alone. MMGBSA analysis corroborates the MD simulation result and showed the lowest energy for the compounds 2734589 and 138968421. The DFT and MEP analysis also predicts the better reactivity and stability of both the hit compounds. Overall, both andrographolide derivatives exhibit good potential as potent inhibitors for both nsp14 and nsp16 proteins, however, in-vitro and in vivo assessment would be required to prove their efficacy and safety in clinical settings. Moreover, the drug discovery strategy aiming at the dual target approach might serve as a useful model for inventing novel drug molecules for various other diseases.

Unraveling the Properties of Phage Display Fab Libraries and Their Use in the Selection of Gliadin-Specific Probes by Applying High-Throughput Nanopore Sequencing.

Directed evolution is a pivotal strategy for new antibody discovery, which allowed the generation of high-affinity Fabs against gliadin from two antibody libraries in our previous studies. One of the libraries was exclusively derived from celiac patients' mRNA (immune library) while the other was obtained through a protein engineering approach (semi-immune library). Recent advances in high-throughput DNA sequencing techniques are revolutionizing research across genomics, epigenomics, and transcriptomics. In the present work, an Oxford Nanopore in-lab sequencing device was used to comprehensively characterize the composition of the constructed libraries, both at the beginning and throughout the phage-mediated selection processes against gliadin. A customized analysis pipeline was used to select high-quality reads, annotate chain distribution, perform sequence analysis, and conduct statistical comparisons between the different selection rounds. Some immunological attributes of the most representative phage variants after the selection process were also determined. Sequencing results revealed the successful transfer of the celiac immune response features to the immune library and the antibodies derived from it, suggesting the crucial role of these features in guiding the selection of high-affinity recombinant Fabs against gliadin. In summary, high-throughput DNA sequencing has improved our understanding of the selection processes aimed at generating molecular binders against gliadin.

Straightforward access to C2-formyl indoles using an oxidative combination of N-chlorosuccinimide and dimethyl sulfoxide

C2-formyl indoles are important building blocks for the organic synthesis of alkaloids and small molecules with broad biological activities. Generally, its preparation involves both direct formylation of indoles at the activated C2-position with as unstable carbonyl sources and oxidative cleavage of tedious prepared tetrahydro-β-carbolines (THβCs) in the harsh conditions. In continuation of our research on the oxidative functionalization of indoles, in this letter, we report that straightforward effectively oxidation of easily available 2-methylindoles bearing C3-heteroatom functional group proceeds regioselectively C2-formyl indoles in good and excellent yields employing an oxidative combination of NCS/DMSO. This methodology presents a reasonably broad substrate scope and excellent functional group tolerance, thus enabling the preparation of highly versatile building blocks susceptible to further functionalization. An additional feature of this approach includes high efficiency, ease of operation, facile purification, and short reaction time, making it a promising strategy for organic synthesis and drug discovery.

Discovery of a SUCNR1 antagonist for potential treatment of diabetic nephropathy: In silico and in vitro studies

Diabetic nephropathy (DN) is one of the most severe complications of diabetes mellitus. Succinate Receptor 1 (SUCNR1), a member of the G-protein-coupled receptor (GPCR) family, represents a potential target for treatment of DN. Here, utilizing multi-strategy in silico virtual screening methods containing AlphaFold2 modelling, molecular dynamics (MD) simulation, ligand-based pharmacophore screening, molecular docking and machine learning-based similarity clustering, we successfully identified a novel antagonist of SUCNR1, AK-968/12117473 (Cpd3). Through extensive in vitro experiments, including dual-luciferase reporter assay, cellular thermal shift assay, immunofluorescence, and western blotting, we substantiated that Cpd3 could specifically target SUCNR1, inhibit the activation of NF-κB pathway, and ameliorate epithelial-mesenchymal transition (EMT) and extracellular matrix (ECM) deposition in renal tubular epithelial cells (NRK-52E) under high glucose conditions. Further in silico simulations revealed the molecular basis of the SUCNR1-Cpd3 interaction, and the in vitro metabolic stability assay indicated favorable drug-like pharmacokinetic properties of Cpd3. This work not only successfully pinpointed Cpd3 as a specific antagonist of SUCNR1 to serve as a promising candidate in the realm of therapeutic interventions for DN, but also provides a paradigm of dry-wet combined discovery strategies for GPCR-based therapeutics.

Design, Synthesis, Formulation, and Bioevaluation of Trisubstituted Triazines as Highly Selective mTOR Inhibitors for the Treatment of Human Breast Cancer.

The aberrant activation of the PI3K/mTOR signaling pathway is implicated in various human cancers. Thus, the development of inhibitors targeting mTOR has attracted considerable attention. In this study, we used a structure-based drug design strategy to discover a highly potent and kinase-selective mTOR inhibitor 24 (PT-88), which demonstrated an mTOR inhibitory IC50 value of 1.2 nM without obvious inhibition against another 195 kinases from the kinase profiling screening. PT-88 displayed selective inhibition against MCF-7 cells (IC50: 0.74 μM) with high biosafety against normal cells, in which autophagy induced by mTOR inhibition was implicated. After successful encapsulation in a lipodisc formulation, PT-88 demonstrated favorable pharmacokinetic and biosafety profiles and exerted a large antitumor effect in an MCF-7 subcutaneous bearing nude mice model. Our study shows the discovery of a highly selective mTOR inhibitor using a structure-based drug discovery strategy and provides a promising antitumor candidate for future study and development.

Artificial Intelligence-Assisted Optimization of Antipigmentation Tyrosinase Inhibitors: De Novo Molecular Generation Based on a Low Activity Lead Compound.

Artificial intelligence (AI) de novo molecular generation is a highly promising strategy in the drug discovery, with deep reinforcement learning (RL) models emerging as powerful tools. This study introduces a fragment-by-fragment growth RL forward molecular generation and optimization strategy based on a low activity lead compound. This process integrates fragment growth-based reaction templates, while target docking and drug-likeness prediction were simultaneously performed. This comprehensive approach considers molecular similarity, internal diversity, synthesizability, and effectiveness, thereby enhancing the quality and efficiency of molecular generation. Finally, a series of tyrosinase inhibitors were generated and synthesized. Most compounds exhibited more improved activity than lead, with an optimal candidate compound surpassing the effects of kojic acid and demonstrating significant antipigmentation activity in a zebrafish model. Furthermore, metabolic stability studies indicated susceptibility to hepatic metabolism. The proposed AI structural optimization strategies will play a promising role in accelerating the drug discovery and improving traditional efficiency.

The Prediction of LptA and LptC Protein-Protein Interactions and Virtual Screening for Potential Inhibitors.

Antibiotic resistance in Gram-negative bacteria remains one of the most pressing challenges to global public health. Blocking the transportation of lipopolysaccharides (LPS), a crucial component of the outer membrane of Gram-negative bacteria, is considered a promising strategy for drug discovery. In the transportation process of LPS, two components of the LPS transport (Lpt) complex, LptA and LptC, are responsible for shuttling LPS across the periplasm to the outer membrane, highlighting their potential as targets for antibacterial drug development. In the current study, a protein-protein interaction (PPI) model of LptA and LptC was constructed, and a molecular screening strategy was employed to search a protein-protein interaction compound library. The screening results indicated that compound 18593 exhibits favorable binding free energy with LptA and LptC. In comparison with the molecular dynamics (MD) simulations on currently known inhibitors, compound 18593 shows more stable target binding ability at the same level. The current study suggests that compound 18593 may exhibit an inhibitory effect on the LPS transport process, making it a promising hit compound for further research.

Beyond the β-amino alcohols framework: identification of novel β-hydroxy pyridinium salt-decorated pterostilbene derivatives as bacterial virulence factor inhibitors.

Bacterial virulence factors are involved in various biological processes and mediate persistent bacterial infections. Focusing on virulence factors of phytopathogenic bacteria is an attractive strategy and crucial direction in pesticide discovery to prevent invasive and persistent bacterial infection. Hence, discovery and development of novel agrochemicals with high activity, low-risk, and potent anti-virulence is urgently needed to control plant bacterial diseases. A series of novel β-hydroxy pyridinium cation decorated pterostilbene derivatives were prepared and their antibacterial activities against Xanthomonas oryzae pv. oryzae (Xoo) were systematacially assessed. Among these pterostilbene derivatives, compound 4S exhibited the best antibacterial activity against Xoo in vitro, with an half maximal effective concentration (EC50) value of 0.28 μg mL-1. A series of biochemical assays including scanning electron microscopy, crystal violet staining, and analysis of biofilm formation, swimming motility, and related virulence factor gene expression levels demonstrated that compound 4S could function as a new anti-virulence factor inhibitor by interfering with the bacterial infection process. Furthermore, the pot experiments provided convinced evidence that compound 4S had the high control efficacy (curative activity: 71.4%, protective activity: 72.6%), and could be used to effectively manage rice bacterial leaf blight in vivo. Compounds 4S is an attractive virulence factor inhibitor with potential for application in treating plant bacterial diseases by suppressing production of several virulence factors. © 2024 Society of Chemical Industry.

Leptin’s Inverse Association With Brain Morphology and Depressive Symptoms: A Discovery and Confirmatory Study Across 2 Independent Samples

BackgroundMajor depressive disorder has a complex, bidirectional relationship with metabolic dysfunction, but the neural correlates of this association are not well understood. MethodsIn this cross-sectional investigation, we used a 2-step discovery and confirmatory strategy utilizing 2 independent samples (sample 1: 288 participants, sample 2: 196 participants) to examine the association between circulating indicators of metabolic health (leptin and adiponectin) and brain structures in individuals with major depressive disorder. ResultsWe found a replicable inverse correlation between leptin levels and cortical surface area within essential brain areas responsible for emotion regulation, such as the left posterior cingulate cortex, right pars orbitalis, right superior temporal gyrus, and right insula (standardized beta coefficient range: −0.27 to −0.49, puncorrected < .05). Notably, this relationship was independent of C-reactive protein levels. We also identified a significant interaction effect of leptin levels and diagnosis on the cortical surface area of the right superior temporal gyrus (standardized beta coefficient = 0.26 in sample 1, standardized beta coefficient = 0.30 in sample 2, puncorrected < .05). We also observed a positive correlation between leptin levels and atypical depressive symptoms in both major depressive disorder groups (r = 0.14 in sample 1, r = 0.29 in sample 2, puncorrected < .05). ConclusionsThe inverse association between leptin and cortical surface area in brain regions that are important for emotion processing and leptin’s association with atypical depressive symptoms support the hypothesis that metabolic processes may be related to emotion regulation. However, the molecular mechanisms through which leptin may exert these effects should be explored further.

Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling

Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, poses a significant threat due to its intricate genetic variability, rendering it challenging to diagnose accurately. Various therapies such as immunotherapy, radiotherapy, and diverse chemotherapy approaches like drug repurposing and combination therapy are widely used depending on cancer subtype and metastasis severity. Our study revolves around an innovative drug discovery strategy targeting potential drug candidates specific to RTK signalling, a prominently targeted receptor class in cancer. To accomplish this, we have developed a multimodal deep neural network (MM-DNN) based QSAR model integrating omics datasets to elucidate genomic, proteomic expression data, and drug responses, validated rigorously. The results showcase an R2 value of 0.917 and an RMSE value of 0.312, affirming the model's commendable predictive capabilities. Structural analogs of drug molecules specific to RTK signalling were sourced from the PubChem database, followed by meticulous screening to eliminate dissimilar compounds. Leveraging the MM-DNN-based QSAR model, we predicted the biological activity of these molecules, subsequently clustering them into three distinct groups. Feature importance analysis was performed. Consequently, we successfully identified prime drug candidates tailored for each potential downstream regulatory protein within the RTK signalling pathway. This method makes the early stages of drug development faster by removing inactive compounds, providing a hopeful path in combating breast cancer.

Abstract LB393: Exosome based multiomics combined with cfDNA methylation reveals complementary signatures in blood based liquid biopsy that carry promise for minimally invasive colorectal cancer screening

Abstract Introduction: Colorectal cancer (CRC) ranks second in cancer morbidity, with approximately 60% of patients being diagnosed at an advanced stage. Here, we apply a multiomic strategy jointly leveraging plasma cell-free DNA (cfDNA) and multiple extracellular vesicle (EV)-derived analytes to enhance CRC diagnostic accuracy. Materials and Methods: Plasma was collected from 96 individuals, 48 CRC patients (stage II-IV) and 48 CRC-negative healthy controls. EVs and cfDNA were isolated from the same plasma sample to look at complementary information within the sample. We developed an EV RNA sequencing platform targeting mRNAs and long non-coding RNAs and sequenced to a depth of 50M reads per sample; cfDNA methylome profiling was sequenced to an equivalent depth. The Olink platform profiled proteins both directly from plasma and from isolated EVs. Expression of EV-derived RNA, splice variants, and proteins in addition to cfDNA methylation patterns were analyzed using Bio-Techne’s multiomic platform. Machine learning-based feature selection algorithms identified biomarker signatures from each analyte. Receiver-operator characteristic curves (ROC) were generated utilizing leave-one-out cross-validation of naïve Bayes classifier models to compute the area under the curve (AUC). Individual signatures were integrated to generate a multiomic classifier. Results: Differential gene expression (DEx) analysis identified ~1000 DEx genes with significant enrichment of CRC-relevant pathways. Splice variant analysis identified several genes previously implicated in CRC with differential isoform usage. Over 1.7 million differentially methylated bases were detected between CRC-positive and CRC-negative cohorts. Segmentation of the genome using out-of-bag reference data yielded ~1100 genomic segments, many of which overlap previously reported CRC biomarkers and identified unique information about the CRC status. Proteomic analysis identified several differentially abundant markers with high discriminatory power. Following feature selection analysis, AUCs obtained from top 10 features of RNA expression, splice variants, cfDNA methylation, and protein abundance with an integrative analysis identified a multi-analyte signature, with an overall AUC of 0.99. Conclusion: While biomarker signatures obtained from each analyte in this study resulted in effective separation of CRC status, the multiomic signature further improved the discriminatory power—underscoring the complementary nature of the signals. Thus, a multiomic biomarker discovery strategy leveraging cfDNA and EV cargo holds tremendous promise for minimally invasive screening and potential detection of early-stage cancers, which is currently under further investigation. Citation Format: Kyle Manning, Dulaney Miller, Yang Yang, Jeff Cole, Shuran Xing, Christopher Benway, Christian Ray, Sudipto Chakrabortty, Brian Haynes, Seth Yu, Johan Skog. Exosome based multiomics combined with cfDNA methylation reveals complementary signatures in blood based liquid biopsy that carry promise for minimally invasive colorectal cancer screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB393.

1,2,3-Triazole Hybrids Containing Isatins and Phenolic Moieties: Regioselective Synthesis and Molecular Docking Studies.

The synthesis of hybrid molecules is one of the current strategies of drug discovery for the development of new lead compounds. The 1,2,3-triazole moiety represents an important building block in Medicinal Chemistry, extensively present in recent years. In this paper, we presented the design and the synthesis of new 1,2,3-triazole hybrids, containing both an isatine and a phenolic core. Firstly, the non-commercial azide and the alkyne synthons were prepared by different isatines and phenolic acids, respectively. Then, the highly regioselective synthesis of 1,4-disubstituted triazoles was obtained in excellent yields by a click chemistry approach, catalyzed by Cu(I). Finally, a molecular docking study was performed on the hybrid library, finding four different therapeutic targets. Among them, the most promising results were obtained on 5-lipoxygenase, an enzyme involved in the inflammatory processes.

An integrated framework for prognosis prediction and drug response modeling in colorectal liver metastasis drug discovery

BackgroundColorectal cancer (CRC) is the third most prevalent cancer globally, and liver metastasis (CRLM) is the primary cause of death. Hence, it is essential to discover novel prognostic biomarkers and therapeutic drugs for CRLM.MethodsThis study developed two liver metastasis-associated prognostic signatures based on differentially expressed genes (DEGs) in CRLM. Additionally, we employed an interpretable deep learning model utilizing drug sensitivity databases to identify potential therapeutic drugs for high-risk CRLM patients. Subsequently, in vitro and in vivo experiments were performed to verify the efficacy of these compounds.ResultsThese two prognostic models exhibited superior performance compared to previously reported ones. Obatoclax, a BCL-2 inhibitor, showed significant differential responses between high and low risk groups classified by prognostic models, and demonstrated remarkable effectiveness in both Transwell assay and CT26 colorectal liver metastasis mouse model.ConclusionsThis study highlights the significance of developing specialized prognostication approaches and investigating effective therapeutic drugs for patients with CRLM. The application of a deep learning drug response model provides a new drug discovery strategy for translational medicine in precision oncology.

Fragment-based drug discovery and biological evaluation of novel cannabinol-based inhibitors of oxytosis/ferroptosis for neurological disorders

The oxytosis/ferroptosis regulated cell death pathway is an emerging field of research owing to its pathophysiological relevance to a wide range of neurological disorders, including Alzheimer's and Parkinson's diseases and traumatic brain injury. Developing novel neurotherapeutics to inhibit oxytosis/ferroptosis offers exciting opportunities for the treatment of these and other neurological diseases. Previously, we discovered cannabinol (CBN) as a unique, potent inhibitor of oxytosis/ferroptosis by targeting mitochondria and modulating their function in neuronal cells. To further elucidate which key pharmacophores and chemical space are essential to the beneficial effects of CBN, we herein introduce a fragment-based drug discovery strategy in conjunction with cell-based phenotypic screens using oxytosis/ferroptosis to determine the structure-activity relationship of CBN. The resulting information led to the development of four new CBN analogs, CP1-CP4, that not only preserve the sub-micromolar potency of neuroprotection and mitochondria-modulating activities seen with CBN in neuronal cell models but also have better druglike properties. Moreover, compared to CBN, the analog CP1 shows improved in vivo efficacy in the Drosophila model of mild traumatic brain injury. Together these studies identify the key molecular scaffolds of cannabinoids that contribute to neuroprotection against oxytosis/ferroptosis. They also highlight the advantageous approach of combining in vitro cell-based assays and rapid in vivo studies using Drosophila models for evaluating new therapeutic compounds.

High-Throughput Methods for the Discovery of Small Molecule Modulators of Pancreatic Beta-Cell Function and Regeneration.

The progression of type II diabetes (T2D) is characterized by a complex and highly variable loss of beta-cell mass, resulting in impaired insulin secretion. Many T2D drug discovery efforts aimed at discovering molecules that can protect or restore beta-cell mass and function have been developed using limited beta-cell lines and primary rodent/human pancreatic islets. Various high-throughput screening methods have been used in the context of drug discovery, including luciferase-based reporter assays, glucose-stimulated insulin secretion, and high-content screening. In this context, a cornerstone of small molecule discovery has been the use of immortalized rodent beta-cell lines. Although insightful, this usage has led to a more comprehensive understanding of rodent beta-cell proliferation pathways rather than their human counterparts. Advantages gained in enhanced physiological relevance are offered by three-dimensional (3D) primary islets and pseudoislets in contrast to monolayer cultures, but these approaches have been limited to use in low-throughput experiments. Emerging methods, such as high-throughput 3D islet imaging coupled with machine learning, aim to increase the feasibility of integrating 3D microtissue structures into high-throughput screening. This review explores the current methods used in high-throughput screening for small molecule modulators of beta-cell mass and function, a potentially pivotal strategy for diabetes drug discovery.

The Science and Art of Structure-Based Virtual Screening.

Structure-based virtual screening has gained momentum again as the high attrition rate at every stage of drug discovery drives the need to explore a greater chemical space. From the Bayesian perspective, its shortcomings as a viable strategy for sustainable hit discovery are discussed, with regard to the prior hit rates of screening libraries and the performance of computational methods. Lessons are shared in selecting virtual hits for experimental validation learned from a series of eight successful campaigns, one of which impacted the discovery of a drug candidate currently in clinical trials.

Serendipitous Identification of Azine Anticancer Agents Using a Privileged Scaffold Morphing Strategy.

The use of privileged scaffolds as a starting point for the construction of libraries of bioactive compounds is a widely used strategy in drug discovery and development. Scaffold decoration, morphing and hopping are additional techniques that enable the modification of the chosen privileged framework and better explore the chemical space around it. In this study, two series of highly functionalized pyrimidine and pyridine derivatives were synthesized using a scaffold morphing approach consisting of triazine compounds obtained previously as antiviral agents. Newly synthesized azines were evaluated against lymphoma, hepatocarcinoma, and colon epithelial carcinoma cells, showing in five cases acceptable to good anticancer activity associated with low cytotoxicity on healthy fibroblasts. Finally, ADME in vitro studies were conducted on the best derivatives of the two series showing good passive permeability and resistance to metabolic degradation.

Federated Causality Learning with Explainable Adaptive Optimization

Discovering the causality from observational data is a crucial task in various scientific domains. With increasing awareness of privacy, data are not allowed to be exposed, and it is very hard to learn causal graphs from dispersed data, since these data may have different distributions. In this paper, we propose a federated causal discovery strategy (FedCausal) to learn the unified global causal graph from decentralized heterogeneous data. We design a global optimization formula to naturally aggregate the causal graphs from client data and constrain the acyclicity of the global graph without exposing local data. Unlike other federated causal learning algorithms, FedCausal unifies the local and global optimizations into a complete directed acyclic graph (DAG) learning process with a flexible optimization objective. We prove that this optimization objective has a high interpretability and can adaptively handle homogeneous and heterogeneous data. Experimental results on synthetic and real datasets show that FedCausal can effectively deal with non-independently and identically distributed (non-iid) data and has a superior performance.