Molecular property prediction is a fundamental task in drug discovery and materials science, yet most high-performing approaches depend on large-scale pretraining that demands substantial computational resources. This work proposes a pretraining-free ensemble framework that trains multiple Transformer-based architectures—BERT, RoBERTa, and XLNet—from random initialization using the Atom-in-SMILES (AIS) molecular representation, which provides richer atomic-level semantics than conventional SMILES. The three Transformer encoders are coupled with BiLSTM prediction heads and integrated via a BaggingRegressor to reduce variance and improve generalization. Experiments on the ZINC250k and ZINC... More >

This perspective introduces the range of ethical concerns entailed by the widespread adoption of AI, particularly as they impact human health. It begins by (1) illustrating risks associated with all large-scale AI systems, then moves to (2) corporate and governmental applications of AI that affect human health. It overviews the ways (3) that patient usage of AI has affected human health; (4) that “passive” medical AI (like recording documents) and (5) “active” medical AI (like diagnosing and prescribing) may affect human health. It concludes with (6) reflections on reporting, responsibility, and regulation, wherein international cooperation and governance systems appear essential for... More >