Abstract
We have seen more progress in computational biology for macromolecules in the last five years than we experienced in the five preceding decades. Thus, it is very challenging to forecast future progress. It is possible that we have reached a plateau, and we will be stuck with similar problems as we have today. Still, it is also possible that the field will continue its rapid progress and completely transform other fields, such as biochemistry, molecular and cell biology, and medicine. It is also possible that general AI will take over, and all scientific endeavours will be conducted without human input. To be honest, we do not know what will happen, but we will highlight a few of the challenges and the most critical research questions that we face today. Hopefully, these will be resolved within the following decades, or hopefully much earlier. Looking back over the last decade, we can see that machine learning and deep learning have become significantly more popular (T-test residual > 2) among the papers published within our section of PlosCB. We do believe that this trend will continue; therefore, we focus on the challenges that must be overcome for it to make significant and notable contributions. The future of computational biology for macromolecules in 20 years is likely to be characterised by transformative advances in accuracy, automation, integration, and explainability, with AI playing a role in one form or another.
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