Copper-Catalyzed Asymmetric Hydrogenation of Unsymmetrical ortho-Br Substituted Benzophenones.

The asymmetric hydrogenation of benzophenones, catalyzed by low-activity earth-abundant metal copper, has hitherto remained a challenge due to the substrates equipped with two indistinguishably similar aryl groups. In this study, we demonstrated that the prochiral carbon of the ortho-bromine substrate exhibits the highest electrophilicity and high reactivity among the ortho-halogen substituted benzophenones, as determined by the Fukui function (f+) analysis and hydrogenation reaction. Considering that the enantiodirecting functional bromine group can be easily derivatized and removed in the products, we successfully achieved a green copper-catalyzed asymmetric hydrogenation of ortho-bromine substituted benzophenones. This method yielded a series of chiral benzhydrols with excellent results. The utility of this protocol has been validated through a gram-scale reaction and subsequent product transformations. Hirshfeld partition (IGMH) and energy decomposition analysis (EDA) indicate that the CH···HC multiple attractive dispersion interactions (MADI) effect between the catalyst and substrate enhances the catalyst's activity.

Dual-functional application of Ca2Ta2O7:Bi3+/Eu3+ phosphors in multicolor tunable optical thermometry and WLED.

A series of Bi3+/Eu3+ co-doped Ca2Ta2O7 (CTO:Bi3+/Eu3+) phosphors were prepared by high-temperature solid-state method for dual-emission center optical thermometers and white light-emitting diode (WLED) device. By modulating the doping ratio of Bi3+/Eu3+ and utilizing the energy transfer from Bi3+ to Eu3+, the tunable color emission ranging from green to reddish-orange was realized. The designed CTO:0.04Bi3+/Eu3+ optical thermometers exhibit significant thermochromism, superior stability, and repeatability, with maximum sensitivities of Sa = 0.055 K-1 (at 510 K) and Sr = 1.298% K-1 (at 480 K) within the temperature range of 300-510 K, owing to the different thermal quenching behaviors between Bi3+ and Eu3+ ions. These features indicate the potential application prospects of the prepared samples in visualized thermometer or high-temperature safety marking. Furthermore, leveraging the excellent zero-thermal-quenching performance, outstanding acid/alkali resistance, and color stability of CTO:0.04Bi3+/0.16Eu3+ phosphor, a WLED device with a high Ra value of 95.3 has been realized through its combination with commercially available blue and green phosphors, thereby demonstrating the potential application of CTO:0.04Bi3+/0.16Eu3+ in near-UV pumped WLED devices.

Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning.

Background: Chemicals may lead to acute liver injuries, posing a serious threat to human health. Achieving the precise safety profile of a compound is challenging due to the complex and expensive testing procedures. In silico approaches will aid in identifying the potential risk of drug candidates in the initial stage of drug development and thus mitigating the developmental cost. Methods: In current studies, QSAR models were developed for hepatotoxicity predictions using the ensemble strategy to integrate machine learning (ML) and deep learning (DL) algorithms using various molecular features. A large dataset of 2588 chemicals and drugs was randomly divided into training (80%) and test (20%) sets, followed by the training of individual base models using diverse machine learning or deep learning based on three different kinds of descriptors and fingerprints. Feature selection approaches were employed to proceed with model optimizations based on the model performance. Hybrid ensemble approaches were further utilized to determine the method with the best performance. Results: The voting ensemble classifier emerged as the optimal model, achieving an excellent prediction accuracy of 80.26%, AUC of 82.84%, and recall of over 93% followed by bagging and stacking ensemble classifiers method. The model was further verified by an external test set, internal 10-fold cross-validation, and rigorous benchmark training, exhibiting much better reliability than the published models. Conclusion: The proposed ensemble model offers a dependable assessment with a good performance for the prediction regarding the risk of chemicals and drugs to induce liver damage.

FGTN: Fragment-based graph transformer network for predicting reproductive toxicity.

Reproductive toxicity is one of the important issues in chemical safety. Traditional laboratory testing methods are costly and time-consuming with raised ethical issues. Only a few in silico models have been reported to predict human reproductive toxicity, but none of them make full use of the topological information of compounds. In addition, most existing atom-based graph neural network methods focus on attributing model predictions to individual nodes or edges rather than chemically meaningful fragments or substructures. In current studies, we develop a novel fragment-based graph transformer network (FGTN) approach to generate the QSAR model of human reproductive toxicity by considering internal topological structure information of compounds. In the FGTN model, the compound is represented by a graph architecture using fragments to be nodes and bonds linking two fragments to be edges. A super molecule-level node is further proposed to connect all fragment nodes by undirected edges, obtaining global molecular features from fragment embeddings. The FGTN model achieved an accuracy (ACC) of 0.861 and an area under the receiver operating characteristic curve (AUC) value of 0.914 on nonredundant blind tests, outperforming traditional fingerprint-based machine learning models and atom-based GCN model. The FGTN model can attribute toxic predictions to fragments, generating specific structural alerts for the positive compound. Moreover, FGTN may also have the capability to distinguish various chemical isomers. We believe that FGTN can be used as a reliable and effective tool for human reproductive toxicity prediction in contribution to the advancement of chemical safety assessment.

Conformations of KRAS4B Affected by Its Partner Binding and G12C Mutation: Insights from GaMD Trajectory-Image Transformation-Based Deep Learning.

Binding of partners and mutations highly affects the conformational dynamics of KRAS4B, which is of significance for deeply understanding its function. Gaussian accelerated molecular dynamics (GaMD) simulations followed by deep learning (DL) and principal component analysis (PCA) were carried out to probe the effect of G12C and binding of three partners NF1, RAF1, and SOS1 on the conformation alterations of KRAS4B. DL reveals that G12C and binding of partners result in alterations in the contacts of key structure domains, such as the switch domains SW1 and SW2 together with the loops L4, L5, and P-loop. Binding of NF1, RAF1, and SOS1 constrains the structural fluctuation of SW1, SW2, L4, and L5; on the contrary, G12C leads to the instability of these four structure domains. The analyses of free energy landscapes (FELs) and PCA also show that binding of partners maintains the stability of the conformational states of KRAS4B while G12C induces greater mobility of the switch domains SW1 and SW2, which produces significant impacts on the interactions of GTP with SW1, L4, and L5. Our findings suggest that partner binding and G12C play important roles in the activity and allosteric regulation of KRAS4B, which may theoretically aid in further understanding the function of KRAS4B.

Unveiling Allosteric Regulation and Binding Mechanism of BRD9 through Molecular Dynamics Simulations and Markov Modeling.

Bromodomain-containing protein 9 (BRD9) is a key player in chromatin remodeling and gene expression regulation, and it is closely associated with the development of various diseases, including cancers. Recent studies have indicated that inhibition of BRD9 may have potential value in the treatment of certain cancers. Molecular dynamics (MD) simulations, Markov modeling and principal component analysis were performed to investigate the binding mechanisms of allosteric inhibitor POJ and orthosteric inhibitor 82I to BRD9 and its allosteric regulation. Our results indicate that binding of these two types of inhibitors induces significant structural changes in the protein, particularly in the formation and dissolution of α-helical regions. Markov flux analysis reveals notable changes occurring in the α-helicity near the ZA loop during the inhibitor binding process. Calculations of binding free energies reveal that the cooperation of orthosteric and allosteric inhibitors affects binding ability of inhibitors to BRD9 and modifies the active sites of orthosteric and allosteric positions. This research is expected to provide new insights into the inhibitory mechanism of 82I and POJ on BRD9 and offers a theoretical foundation for development of cancer treatment strategies targeting BRD9.

MULTIMODAL NEURAL CORRELATES OF CHILDHOOD PSYCHOPATHOLOGY.

Complex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples, supporting generalizability, and robust to variations in analytical parameters. Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.

Insights into the Interaction Mechanisms of Peptide and Non-Peptide Inhibitors with MDM2 Using Gaussian-Accelerated Molecular Dynamics Simulations and Deep Learning.

Inhibiting MDM2-p53 interaction is considered an efficient mode of cancer treatment. In our current study, Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and binding free energy calculations were combined together to probe the binding mechanism of non-peptide inhibitors K23 and 0Y7 and peptide ones PDI6W and PDI to MDM2. The GaMD trajectory-based DL approach successfully identified significant functional domains, predominantly located at the helixes α2 and α2', as well as the ß-strands and loops between α2 and α2'. The post-processing analysis of the GaMD simulations indicated that inhibitor binding highly influences the structural flexibility and collective motions of MDM2. Calculations of molecular mechanics-generalized Born surface area (MM-GBSA) and solvated interaction energy (SIE) not only suggest that the ranking of the calculated binding free energies is in agreement with that of the experimental results, but also verify that van der Walls interactions are the primary forces responsible for inhibitor-MDM2 binding. Our findings also indicate that peptide inhibitors yield more interaction contacts with MDM2 compared to non-peptide inhibitors. Principal component analysis (PCA) and free energy landscape (FEL) analysis indicated that the piperidinone inhibitor 0Y7 shows the most pronounced impact on the free energy profiles of MDM2, with the piperidinone inhibitor demonstrating higher fluctuation amplitudes along primary eigenvectors. The hot spots of MDM2 revealed by residue-based free energy estimation provide target sites for drug design toward MDM2. This study is expected to provide useful theoretical aid for the development of selective inhibitors of MDM2 family members.

Unveiling Conformational States of CDK6 Caused by Binding of Vcyclin Protein and Inhibitor by Combining Gaussian Accelerated Molecular Dynamics and Deep Learning.

CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transitions of CDK6 were identified by using Gaussian accelerated molecular dynamics (GaMD), deep learning (DL), and free energy landscapes (FELs). DL finds that the binding pocket as well as the T-loop binding to the Vcyclin protein are involved in obvious differences of conformation contacts. This result suggests that the binding pocket of inhibitors (LQQ and AP9) and the binding interface of CDK6 to the Vcyclin protein play a key role in the function of CDK6. The analyses of FELs reveal that the binding pocket and the T-loop of CDK6 have disordered states. The results from principal component analysis (PCA) indicate that the binding of the Vcyclin protein affects the fluctuation behavior of the T-loop in CDK6. Our QM/MM-GBSA calculations suggest that the binding ability of LQQ to CDK6 is stronger than AP9 with or without the binding of the Vcyclin protein. Interaction networks of inhibitors with CDK6 were analyzed and the results reveal that LQQ contributes more hydrogen binding interactions (HBIs) and hot interaction spots with CDK6. In addition, the binding pocket endures flexibility changes from opening to closing states and the Vcyclin protein plays an important role in the stabilizing conformation of the T-loop. We anticipate that this work could provide useful information for further understanding the function of CDK6 and developing new promising inhibitors targeting CDK6.

Nickel-catalyzed asymmetric hydrogenation for the preparation of α-substituted propionic acids.

Transition metal-catalyzed asymmetric hydrogenation is one of the most efficient methods for the preparation of chiral α-substituted propionic acids. However, research on this method, employing cleaner earth-abundant metal catalysts, is still insufficient in both academic and industrial contexts. Herein, we report an efficient nickel-catalyzed asymmetric hydrogenation of α-substituted acrylic acids affording the corresponding chiral α-substituted propionic acids with up to 99.4% ee (enantiomeric excess) and 10,000 S/C (substrate/catalyst). In particular, this method can be used to obtain (R)-dihydroartemisinic acid with 99.8:0.2 dr (diastereomeric ratio) and 5000 S/C, which is an essential intermediate for the preparation of the antimalarial drug Artemisinin. The reaction mechanism has been investigated via experiments and DFT (Density Functional Theory) calculations, which indicate that the protonolysis of the C-Ni bond of the key intermediate via an intramolecular proton transfer from the carboxylic acid group of the substrate, is the rate-determining step.

A firm-push-to-open and light-push-to-lock strategy for a general chemical platform to develop activatable dual-modality NIR-II probes.

Activatable near-infrared (NIR) imaging in the NIR-II range is crucial for deep tissue bioanalyte tracking. However, designing such probes remains challenging due to the limited availability of general chemical strategies. Here, we introduced a foundational platform for activatable probes, using analyte-triggered smart modulation of the π-conjugation system of a NIR-II-emitting rhodamine hybrid. By tuning the nucleophilicity of the ortho-carboxy moiety, we achieved an electronic effect termed "firm-push-to-open and light-push-to-lock," which enables complete spirocyclization of the probe before sensing and allows for efficient zwitterion formation when the light-pushing aniline carbamate trigger is transformed into a firm-pushing aniline. This platform produces dual-modality NIR-II imaging probes with ~50-fold fluorogenic and activatable photoacoustic signals in live mice, surpassing reported probes with generally below 10-fold activatable signals. Demonstrating generality, we successfully designed probes for hydrogen peroxide (H2O2) and hydrogen sulfide (H2S). We envision a widespread adoption of the chemical platform for designing activatable NIR-II probes across diverse applications.

Molecular Mechanism of Phosphorylation-Mediated Impacts on the Conformation Dynamics of GTP-Bound KRAS Probed by GaMD Trajectory-Based Deep Learning.

The phosphorylation of different sites produces a significant effect on the conformational dynamics of KRAS. Gaussian accelerated molecular dynamics (GaMD) simulations were combined with deep learning (DL) to explore the molecular mechanism of the phosphorylation-mediated effect on conformational dynamics of the GTP-bound KRAS. The DL finds that the switch domains are involved in obvious differences in conformation contacts and suggests that the switch domains play a key role in the function of KRAS. The analyses of free energy landscapes (FELs) reveal that the phosphorylation of pY32, pY64, and pY137 leads to more disordered states of the switch domains than the wild-type (WT) KRAS and induces conformational transformations between the closed and open states. The results from principal component analysis (PCA) indicate that principal motions PC1 and PC2 are responsible for the closed and open states of the phosphorylated KRAS. Interaction networks were analyzed and the results verify that the phosphorylation alters interactions of GTP and magnesium ion Mg2+ with the switch domains. It is concluded that the phosphorylation pY32, pY64, and pY137 tune the activity of KRAS through changing conformational dynamics and interactions of the switch domains. We anticipated that this work could provide theoretical aids for deeply understanding the function of KRAS.

Binding Mechanism of Inhibitors to BRD4 and BRD9 Decoded by Multiple Independent Molecular Dynamics Simulations and Deep Learning.

Bromodomain 4 and 9 (BRD4 and BRD9) have been regarded as important targets of drug designs in regard to the treatment of multiple diseases. In our current study, molecular dynamics (MD) simulations, deep learning (DL) and binding free energy calculations are integrated to probe the binding modes of three inhibitors (H1B, JQ1 and TVU) to BRD4 and BRD9. The MD trajectory-based DL successfully identify significant functional function domains, such as BC-loop and ZA-loop. The information from the post-processing analysis of MD simulations indicates that inhibitor binding highly influences the structural flexibility and dynamic behavior of BRD4 and BRD9. The results of the MM-GBSA calculations not only suggest that the binding ability of H1B, JQ1 and TVU to BRD9 are stronger than to BRD4, but they also verify that van der Walls interactions are the primary forces responsible for inhibitor binding. The hot spots of BRD4 and BRD9 revealed by residue-based free energy estimation provide target sites of drug design in regard to BRD4 and BRD9. This work is anticipated to provide useful theoretical aids for the development of selective inhibitors over BRD family members.

MCPNET: Development of an interpretable deep learning model based on multiple conformations of the compound for predicting developmental toxicity.

The development of deep learning models for predicting toxicological endpoints has shown great promise, but one of the challenges in the field is the accuracy and interpretability of these models. The bioactive conformation of a compound plays a critical role for it to bind in the target. It is a big issue to figure out the bioactive conformation in deep learning without the co-crystal structure or highly precise molecular simulations. In this study, we developed a deep learning framework of Multi-Conformation Point Network (MCPNET) to construct classification and regression models, respectively, based on electrostatic potential distributions on vdW surfaces around multiple conformations of the compound using a dataset of compounds with developmental toxicity in zebrafish embryo. MCPNET applied 3D multi-conformational surface point cloud to extract the molecular features for model training, which may be critical for capturing the structural diversity of compounds. The models achieved an accuracy of 85 % on the classification task and R2 of 0.66 on the regression task, outperforming traditional machine learning models and other deep learning models. The key feature of our model is its interpretability with the component visualization to identify the factors contributing to the prediction and to understand the compound action mechanism. MCPNET may predict the conformation quietly close to the bioactive conformation of a compound by attention-based multi-conformation pooling mechanism. Our results demonstrated the potential of deep learning based on 3D molecular representations in accurately predicting developmental toxicity. The source code is publicly available at https://github.com/Superlit-CC/MCPNET.

3D-QSAR pharmacophore modeling, virtual screening, molecular docking, MD simulations, in vitro and in vivo studies to identify potential anti-hyperplasia drugs.

Psoriasis is a common immune-mediated skin condition characterized by aberrant keratinocytes and cell proliferation. The purpose of this study was to explore the FDA-approved drugs by 3D-QSAR pharmacophore model and evaluate their efficiency by in-silico, in vitro, and in vivo psoriasis animal model. A 3D-QSAR pharmacophore model was developed by utilizing HypoGen algorithm using the structural features of 48 diaryl derivatives with diverse molecular patterns. The model was validated by a test set of 27 compounds, by cost analysis method, and Fischer's randomization test. The correlation coefficient of the best model (Hypo2) was 0.9601 for the training set while it was 0.805 for the test set. The selected model was taken as a 3D query for the virtual screening of over 3000 FDA-approved drugs. Compounds mapped with the pharmacophore model were further screened through molecular docking. The hits that showed the best docking results were screened through in silico skin toxicity approach. Top five hits were selected for the MD simulation studies. Based on MD simulations results, the best two hit molecules, that is, ebastine (Ebs) and mebeverine (Mbv) were selected for in vitro and in vivo antioxidant studies performed in mice. TNF-α and COX pro-inflammatory mediators, biochemical assays, histopathological analyses, and immunohistochemistry observations confirmed the anti-inflammatory response of the selected drugs. Based on these findings, it appeared that Ebs can effectively treat psoriasis-like skin lesions and down-regulate inflammatory responses which was consistent with docking predictions and could potentially be employed for further research on inflammation-related skin illnesses such as psoriasis.

Skin-Conformable Flexible and Stretchable Ultrasound Transducer for Wearable Imaging.

Ultrasound imaging offers a noninvasive, radiation-free method for visualizing internal tissues and organs, with deep penetration capabilities. This has established it as a crucial tool for physicians in diagnosing internal tissue pathologies and monitoring human conditions. Nonetheless, conventional ultrasound probes are often characterized by their rigidity and bulkiness. Designing a transducer that can seamlessly adapt to the contours and dynamics of soft, curved human skin presents significant technical hurdles. We present a novel flexible and stretchable ultrasound transducer (FSUT) designed for adaptability to large-curvature surfaces while preserving superior imaging quality. Central to this breakthrough is the innovative use of screen-printed silver nanowires (AgNWs) coupled with a composite elastic substrate, together ensuring robust and stable electrical and mechanical connections. Standard tensile and fatigue tests verify its durability. The mechanical, electrical, and acoustic properties of FSUTs are characterized using standard methods, with large tensile strains (≥110%), high flexibility ( R ≥ 1.4 mm), and lightweight ( ≤ 1.58 g) to meet the needs of wearable devices. Center frequency and -6-dB bandwidth are approximately 5.3 MHz and 66.47%, respectively. Images of the commercial anechoic cyst phantom yielded an axial and lateral resolution (depths of 10-70 mm) of approximately 0.31 and 0.46, and 0.34 and 0.84 mm, respectively. The complex curved surface imaging capabilities of FSUT were tested on agar-gelatin-based breast cyst phantoms under different curvatures. Finally, ultrasound images of the thyroid, brachial, and carotid arteries were also obtained from volunteer wearing FSUT.

Roles of Accelerated Molecular Dynamics Simulations in Predictions of Binding Kinetic Parameters.

Rational predictions on binding kinetics parameters of drugs to targets play significant roles in future drug designs. Full conformational samplings of targets are requisite for accurate predictions of binding kinetic parameters. In this review, we mainly focus on the applications of enhanced sampling technologies in calculations of binding kinetics parameters and residence time of drugs. The methods involved in molecular dynamics simulations are applied to not only probe conformational changes of targets but also reveal calculations of residence time that is significant for drug efficiency. For this review, special attention are paid to accelerated molecular dynamics (aMD) and Gaussian aMD (GaMD) simulations that have been adopted to predict the association or disassociation rate constant. We also expect that this review can provide useful information for future drug design.

Shotgun lipidomics of human subretinal fluids under rod-dominant retina reveals cone-dominated lipids.

Subretinal fluid (SRF) accumulates between photoreceptor outer segments and retinal pigment epithelium during rhegmatogenous retinal detachment. Biomolecular components such as lipids originate from cells surrounding the SRF. Knowledge of the composition of these molecules in SRF potentially provides mechanistic insight into the physiologic transfer of lipids between retinal tissue compartments. Using mass spectrometry and tandem mass spectrometry analysis on an electrospray ionization quadrupole-time-of-flight mass spectrometer, we identified a total of 115 lipid molecular species of 11 subclasses and 9 classes in two samples from two patients with rhegmatogenous retinal detachment. These included 47 glycerophosphocholines, 6 glycerophosphoethanolamines, 1 glycerophosphoinositol, 18 sphingomyelins, 9 cholesteryl esters, free cholesterol, 3 ceramides, 22 triacylglycerols and 8 free fatty acids. Glycerophosphocholines were of the highest intensity. By minimizing the formation of different adduct forms or clustering ions of different adducts, we determined the relative intensity of lipid molecular species within the same subclasses. The profiles were compared with those of retinal cells available in the published literature. The glycerophosphocholine profile of SRF was similar to that of cone outer segments, suggesting that outer segment degradation products are constitutively released into the interphotoreceptor matrix, appearing in SRF during detachment. This hypothesis was supported by the retinal distributions of corresponding lipid synthases' mRNA expression obtained from an online resource based on publicly available single-cell sequencing data. In contrast, based on lipid profiles and relevant gene expression in this study, the sources of free cholesterol and cholesteryl esters in SRF appeared more ambiguous, possibly reflecting that outer retina takes up plasma lipoproteins. Further studies to identify and quantify lipids in SRF will help better understand etiology of diseases relevant to outer retina.

The switch states of the GDP-bound HRAS affected by point mutations: a study from Gaussian accelerated molecular dynamics simulations and free energy landscapes.

Point mutations play a vital role in the conformational transformation of HRAS. In this work, Gaussian accelerated molecular dynamics (GaMD) simulations followed by constructions of free energy landscapes (FELs) were adopted to explore the effect of mutations D33K, A59T and L120A on conformation states of the GDP-bound HRAS. The results from the post-processing analyses on GaMD trajectories suggest that mutations alter the flexibility and motion modes of the switch domains from HRAS. The analyses from FELs show that mutations induce more disordered states of the switch domains and affect interactions of GDP with HRAS, implying that mutations yield a vital effect on the binding of HRAS to effectors. The GDP-residue interaction network revealed by our current work indicates that salt bridges and hydrogen bonding interactions (HBIs) play key roles in the binding of GDP to HRAS. Furthermore, instability in the interactions of magnesium ions and GDP with the switch SI leads to the extreme disorder of the switch domains. This study is expected to provide the energetic basis and molecular mechanism for further understanding the function of HRAS.Communicated by Ramaswamy H. Sarma.

Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data.

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.