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Accurate interatomic energies and forces enable high-quality molecular dynamics simulations, torsion scans, potential energy surface mappings, and geometry optimizations. Machine learning algorithms have enabled rapid estimates of the energies and forces with high accuracy. Further development of machine learning algorithms holds promise for producing universal potentials that support many different atomic species. We present the Transformer Interatomic Potential (TrIP): a chemically sound potential based on the SE(3)-Transformer. TrIP’s species-agnostic architecture, which uses continuous atomic representation and homogeneous graph convolutions, encourages parameter sharing between atomic species for more general representations of chemical environments, maintains a reasonable number of parameters, serves as a form of regularization, and is a step toward accurate universal interatomic potentials. TrIP achieves state-of-the-art accuracies on the COMP6 benchmark with an energy prediction of just 1.02 kcal/mol MAE. We introduce physical bias in the form of Ziegler–Biersack–Littmark-screened nuclear repulsion and constrained atomization energies. An energy scan of a water molecule demonstrates that these changes improve long- and near-range interactions compared to other neural network potentials. TrIP also demonstrates stability in molecular dynamics simulations, demonstrating reasonable exploration of Ramachandran space.

Various approaches have used neural networks as probabilistic models for the design of protein sequences. These “inverse folding” models employ different objective functions, which come with trade-offs that have not been assessed in detail before. This study introduces probabilistic definitions of protein stability and conformational specificity and demonstrates the relationship between these chemical properties and the p(structure|seq) Boltzmann probability objective. This links the Boltzmann probability objective function to experimentally verifiable outcomes. We propose a novel sequence decoding algorithm, referred to as “BayesDesign”, that leverages Bayes’ Rule to maximize the p(structure|seq) objective instead of the p(seq|structure) objective common in inverse folding models. The efficacy of BayesDesign is evaluated in the context of two protein model systems, the NanoLuc enzyme and the WW structural motif. Both BayesDesign and the baseline ProteinMPNN algorithm increase the thermostability of NanoLuc and increase the conformational specificity of WW. The possible sources of error in the model are analyzed.

Objectives: Evaluate the gland-level annotations in the PANDA Dataset and provide specific recommendations for the development of an improved prostate adenocarcinoma dataset. Provide insight into why currently developed artificial intelligence (AI) algorithms designed for automatic prostate adenocarcinoma detection have failed to be clinically applicable.

Methods: A neural network model was trained on 5009 Whole Slide Images (WSIs) from PANDA. One expert pathologist repeatedly performed gland-level annotations on 50 PANDA WSIs to create a test set and estimate an intra-pathologist variability value. Dataset labels, expert annotations, and model predictions were compared and analyzed.

Results: We found an intra-pathologist accuracy of 0.83 and Prevalence-Adjusted Bias-Adjusted Kappa (kappa) value of 0.65. The model predictions and dataset labels comparison yielded 0.82 accuracy and 0.64 kappa. The model predictions and dataset labels showed low concordance with the expert pathologist.

Conclusions: Simple AI models trained on PANDA achieve accuracies comparable to intra-pathologist accuracies. Due to variability within or between pathologists these models will unlikely find clinically application. A shift in dataset curation must take place. We urge for the creation of a dataset with multiple annotations from a group of experts. This will enable AI models, trained on this dataset, to produce panel opinions which augment pathological decision making.