AlphaFold2 (AF2) has made significant strides in computational structural biology and drug discovery. However, limitations remain, particularly for downstream tasks such as molecular docking. We propose inaccuracies in amino acid sidechain prediction could contribute to these limitations. To address this, we explored two simple and complementary strategies to improve sidechain accuracy in AF2: (1) substituting the default ResNet-based angle predictor in AlphaFold2 with a Transformer-like model, and (2) refining the angle predictor using an energy-like loss function. Our analysis indicates that ResNets and Transformers offer comparable performance. However, training with an energy-like loss can sometimes boost structural quality, especially when the entire model is finetuned. We suggest a holistic approach that looks beyond AF2's sidechain torsion angle predictor to improve sidechain modeling in future studies.