The Graph Convolutional Network with Multi-representation Alignment for Drug Synergy Prediction

Drug combination refers to the use of two or more drugs to treat a specific disease at the same time. It is currently the mainstream way to treat complex diseases. Compared with single drugs, drug combinations have better efficacy and can better inhibit toxicity and drug resistance. The computational model based on deep learning concatenates the representation of multiple drugs and the corresponding cell line feature as input, and the output is whether the drug combination can have an inhibitory effect on the cell line. However, this strategy of concatenating multiple representations has the following defects: the alignment of drug representation and cell line representation is ignored, resulting in the synergistic relationship not being reflected positionally in the embedding space. Moreover, the alignment measurement function in deep learning cannot be suitable for drug synergy prediction tasks due to differences in input types. Therefore, in this work, we propose a graph convolutional network with multi-representation alignment (GCNMRA) for predicting drug synergy. In the GCNMRA model, we designed a multi-representation alignment function suitable for the drug synergy prediction task so that the positional relationship between drug representations and cell line representation is reflected in the embedding space. In addition, the vector modulus of drug representations and cell line representation is considered to improve the accuracy of calculation results and accelerate model convergence. Finally, many relevant experiments were run on multiple drug synergy datasets to verify the effectiveness of the above innovative elements and the excellence of the GCNMRA model.Comment: 14 pages

GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction

Drug-target binding affinity prediction plays an important role in the early stages of drug discovery, which can infer the strength of interactions between new drugs and new targets. However, the performance of previous computational models is limited by the following drawbacks. The learning of drug representation relies only on supervised data, without taking into account the information contained in the molecular graph itself. Moreover, most previous studies tended to design complicated representation learning module, while uniformity, which is used to measure representation quality, is ignored. In this study, we propose GraphCL-DTA, a graph contrastive learning with molecular semantics for drug-target binding affinity prediction. In GraphCL-DTA, we design a graph contrastive learning framework for molecular graphs to learn drug representations, so that the semantics of molecular graphs are preserved. Through this graph contrastive framework, a more essential and effective drug representation can be learned without additional supervised data. Next, we design a new loss function that can be directly used to smoothly adjust the uniformity of drug and target representations. By directly optimizing the uniformity of representations, the representation quality of drugs and targets can be improved. The effectiveness of the above innovative elements is verified on two real datasets, KIBA and Davis. The excellent performance of GraphCL-DTA on the above datasets suggests its superiority to the state-of-the-art model.Comment: 13 pages, 4 figures, 5 table

Chromospherically Active Stars. VI. HD 136901 = UV CrB: A Massive Ellipsoidal K Giant Single-Lined Spectroscopic Binary

The variable star HD 136901 = UV CrB is a chromospherically active K2 III single-lined spectroscopic binary with an orbital period of 18.665 days. It has modest-strength Ca H and K emission and UV features, while H-alpha is a strong absorption feature containing little or no emission. The inclination of the system is 53 + or - 12 deg. The v sin i of the primary is 42 + or - 2 km/s, resulting in a minimum radius of 15.5 + or - 0.8 solar. When compared with the Roche lobe radius, this results in a mass ratio of 2.90 or larger. Additional constraints indicate that the secondary has a mass between 0.85 and 1.25 solar. Thus, the mass of the primary is at least 2.5 solar and probably is in the range 2.5-4 solar

Speckle and Spectroscopic Orbits of the Early A-Type Triple System in Virginis

Eta Virginis is a bright (V = 3.89) triple system of composite spectral type A2 IV that has been observed for over a dozen years with both spectroscopy and speckle interferometry. Analysis of the speckle observations results in a long period of 13.1 yr. This period is also detected in residuals from the spectroscopic observations of the 71.7919 day short-period orbit. Elements of the long-period orbit were determined separately using the observations of both techniques. The more accurate elements from the speckle solution have been assumed in a simultaneous spectroscopic determination of the short- and long-period orbital elements. The magnitude difference of the speckle components suggests that lines of the third star should be visible in the spectrum