Network structure and detailed parameters of SS-3D-Clump. The SS-3D-Clump receives a 30 px × 30 px × 30 px cube as input and has two neurons in the output layer, representing the two-class verification of molecular clumps. It comprises a 3D CNN and an FCN. The input data in SS-3D-Clump undergo processing steps including ‘Stem’, ‘ResB 1’, and Conv3D. The resulting data are then flattened and passed into the FCN to generate the final output. ‘Stem’ comprises 16 3D Convolution kernels of size 3 and stride 2, and is followed by the ReLU activation function, and 32 MaxPooling layers with a kernel size of 2 and stride of 2. ‘ResB 1’ consists of alternating 3D CNN and Batch Normalization layers, with the ReLU activation function applied in each layer; it incorporates residual connections inspired by the ResNet architecture. The FCN consists of five layers with the following numbers of neurons: 4096, 512, 256, 16, and 2.