Akeyconsideration inbuilding neural models forthistaskisaligning model design with strategies adopted by chemists.

For patients with inflammatory bowel disease, antibiotics can be a double-edged sword. The broad-spectrum drugs often prescribed for gut flare-ups can kill helpful microbes alongside harmful ones, sometimes worsening symptoms over time. When fighting gut inflammation, you don't always want to bring a sledgehammer to a knife fight. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and McMaster University have identified a new compound that takes a more targeted approach. The molecule, called enterololin, suppresses a group of bacteria linked to Crohn's disease flare-ups while leaving the rest of the microbiome largely intact.

Counterfactual data augmentation (CDA) is a method for controlling information or biases in training datasets by generating a complementary dataset with typically opposing biases. Prior work often either relies on hand-crafted rules or algorithmic CDA methods which can leave unwanted information in the augmented dataset. In this work, we show iterative CDA (ICDA) with initial, high-noise interventions can converge to a state with significantly lower noise. Our ICDA procedure produces a dataset where one target signal in the training dataset maintains high mutual information with a corresponding label and the information of spurious signals are reduced. We show training on the augmented datasets produces rationales on documents that better align with human annotation. Our experiments include six human produced datasets and two large-language model generated datasets.

Drugs play a central role in modern medicine, but bringing new ones to market is a lengthy, expensive process. Pharmaceutical companies are exploring ways to streamline all aspects of their complex pipelines with artificial intelligence (AI). A key early step is the discovery and design of new molecules that have a desired biochemical effect that can modulate known disease-related processes. To succeed, the molecules must also be suitable for manufacture and drug formulation, and have an acceptably low number of side effects. Finding better candidates and eliminating losers at an early stage makes this process faster and cheaper.

Now, we trust the complex processes underlying artificial intelligence (AI) with everything from navigation to movie recommendations to targeted advertising. Can we also trust machine learning with our health care? The integration of AI and cancer care was a popular topic in 2021, as evidenced by prominent sessions at two of last year's AACR conferences: the 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved, held virtually October 6-8, 2021, and the San Antonio Breast Cancer Symposium (SABCS), held in a hybrid format December 7-10, 2021. During these sessions, experts gave an overview of how machine learning works, shared data on new applications of AI technologies, and emphasized important considerations for making algorithms equitable. Recognizing that a diverse audience of breast cancer clinicians and researchers may have questions about the fundamentals of AI, the SABCS session "Artificial Intelligence: Beyond the Soundbites" opened with a talk titled, "Everything You Always Wanted to Know About AI But Were Afraid to Ask," presented by Regina Barzilay, PhD, the AI faculty lead at the Jameel Clinic of the Massachusetts Institute of Technology.

The car's underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries. But this won't happen--or shouldn't happen--unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur--and it's inevitable they will. That's one reason Nvidia's car is still experimental.

Efficiently discovering molecules that meet various property requirements can significantly benefit the drug discovery industry. Since it is infeasible to search over the entire chemical space, recent works adopt generative models for goal-directed molecular generation. They tend to utilize the iterative processes, optimizing the parameters of the molecular generative models at each iteration to produce promising molecules for further validation. Assessments are exploited to evaluate the generated molecules at each iteration, providing direction for model optimization. However, most previous works require a massive number of expensive and time-consuming assessments, e.g., wet experiments and molecular dynamic simulations, leading to the lack of practicability. To reduce the assessments in the iterative process, we propose a cost-effective evolution strategy in latent space, which optimizes the molecular latent representation vectors instead. We adopt a pre-trained molecular generative model to map the latent and observation spaces, taking advantage of the large-scale unlabeled molecules to learn chemical knowledge. To further reduce the number of expensive assessments, we introduce a pre-screener as the proxy to the assessments. We conduct extensive experiments on multiple optimization tasks comparing the proposed framework to several advanced techniques, showing that the proposed framework achieves better performance with fewer assessments.

Artificial intelligence is transforming industries around the world -- and health care is no exception. A recent Mayo Clinic study found that AI-enhanced electrocardiograms (ECGs) have the potential to save lives by speeding diagnosis and treatment in patients with heart failure who are seen in the emergency room. The lead author of the study is Demilade "Demi" Adedinsewo, a noninvasive cardiologist at the Mayo Clinic who is actively integrating the latest AI advancements into cardiac care and drawing largely on her learning experience with MIT Professional Education. A dedicated practitioner, Adedinsewo is a Mayo Clinic Florida Women's Health Scholar and director of research for the Cardiovascular Disease Fellowship program. Her clinical research interests include cardiovascular disease prevention, women's heart health, cardiovascular health disparities, and the use of digital tools in cardiovascular disease management.

Explaining the predictions of AI models is paramount in safety-critical applications, such as in legal or medical domains. One form of explanation for a prediction is an extractive rationale, i.e., a subset of features of an instance that lead the model to give its prediction on the instance. Previous works on generating extractive rationales usually employ a two-phase model: a selector that selects the most important features (i.e., the rationale) followed by a predictor that makes the prediction based exclusively on the selected features. One disadvantage of these works is that the main signal for learning to select features comes from the comparison of the answers given by the predictor and the ground-truth answers. In this work, we propose to squeeze more information from the predictor via an information calibration method. More precisely, we train two models jointly: one is a typical neural model that solves the task at hand in an accurate but black-box manner, and the other is a selector-predictor model that additionally produces a rationale for its prediction. The first model is used as a guide to the second model. We use an adversarial-based technique to calibrate the information extracted by the two models such that the difference between them is an indicator of the missed or over-selected features. In addition, for natural language tasks, we propose to use a language-model-based regularizer to encourage the extraction of fluent rationales. Experimental results on a sentiment analysis task as well as on three tasks from the legal domain show the effectiveness of our approach to rationale extraction.

In drug discovery, molecule optimization is an important step in order to modify drug candidates into better ones in terms of desired drug properties. With the recent advance of Artificial Intelligence, this traditionally in vitro process has been increasingly facilitated by in silico approaches. We present an innovative in silico approach to computationally optimizing molecules and formulate the problem as to generate optimized molecular graphs via deep generative models. Our generative models follow the key idea of fragment-based drug design, and optimize molecules by modifying their small fragments. Our models learn how to identify the to-be-optimized fragments and how to modify such fragments by learning from the difference of molecules that have good and bad properties. In optimizing a new molecule, our models apply the learned signals to decode optimized fragments at the predicted location of the fragments. We also construct multiple such models into a pipeline such that each of the models in the pipeline is able to optimize one fragment, and thus the entire pipeline is able to modify multiple fragments of molecule if needed. We compare our models with other state-of-the-art methods on benchmark datasets and demonstrate that our methods significantly outperform others with more than 80% property improvement under moderate molecular similarity constraints, and more than 10% property improvement under high molecular similarity constraints.