With the release of platforms like DALL-E 2 and Midjourney, diffusion generative models have achieved mainstream popularity, owing to their ability to generate a series of absurd, breathtaking, and often meme-worthy images from text prompts like "teddy bears working on new AI research on the moon in the 1980s." But a team of researchers at MIT's Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) thinks there could be more to diffusion generative models than just creating surreal images -- they could accelerate the development of new drugs and reduce the likelihood of adverse side effects. A paper introducing this new molecular docking model, called DiffDock, will be presented at the 11th International Conference on Learning Representations. The model's unique approach to computational drug design is a paradigm shift from current state-of-the-art tools that most pharmaceutical companies use, presenting a major opportunity for an overhaul of the traditional drug development pipeline. Drugs typically function by interacting with the proteins that make up our bodies, or proteins of bacteria and viruses.

Antibodies are small proteins formed by the immune system with the capability of attaching to specific parts of a virus to offset it. As experts continue to fight SARS-CoV-2, the virus that triggered COVID-19, one possible defense route is a synthetic antibody that binds with the spike proteins of the virus to stop the virus from penetrating a human cell. To build an effective synthetic antibody, scientists have to understand precisely how that binding will take place. Proteins, with lumpy 3D structures comprising many folds, can adhere together in millions of combinations, so discovering the right protein complex among virtually countless contenders is very laborious. To simplify the process, MIT scientists developed a machine-learning model that can directly predict the complex that will develop when two proteins stick together.

Equidock, the machine learning system the researchers developed, can directly predict a protein complex like this in a matter of seconds. This deep-learning model can learn these types of interactions from data," says Octavian-Eugen Ganea, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper. The model the researchers developed, called Equidock, focuses on rigid body docking--which occurs when two proteins attach by rotating or translating in 3D space, but their shapes don't squeeze or bend. In addition to using this method with traditional models, the team wants to incorporate specific atomic interactions into Equidock so it can make more accurate predictions. These molecules bind with protein surfaces in specific ways, so rapidly determining how that attachment occurs could shorten the drug development timeline. Equidock, the machine learning system the researchers developed, can directly predict a protein complex like this in a matter of seconds. This deep-learning model can learn these types of interactions from data," says Octavian-Eugen Ganea, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper.

Artificial Intelligence and Machine Learning are increasingly being used to automate decision-making in many sectors within international development. Although computer intelligence is continuously improving, it has been shown that improper implementation of these algorithms can lead to strong bias, unfairness, or exclusion of certain groups. This research project helps determine guidelines of ethical use of machine learning in developing countries, developing a framework of use of machine learning with criteria of fairness and appropriate use, discovering partnerships in industry, academia, or government in developing countries, and building capacity through educational materials and datasets shared with the world at the end of the research. Integral to this effort are case studies of several sites abroad and in the US which focus on different aspects of applications of machine learning, from employment, to medicine, education, lending, devices, to name a few. The output of this research includes a framework for appropriate and ethical use of machine learning methods based on the interdisciplinary case studies, data analyses, meta-analyses, and pedagogical materials which can be integrated into future machine learning courses around the world.

'soft robots constructed from highly compliant materials, similar to those found in living organisms, are being championed as safer, and more adaptable, resilient, and bioinspired alternatives to traditional rigid robots,' comments the official MIT release. 'but giving autonomous control to these deformable robots is a monumental task because they can move in a virtually infinite number of directions at any given moment. 'we're sensorizing soft robots to get feedback for control from sensors, not vision systems, using a very easy, rapid method for fabrication,' he continues. 'we want to use these soft robotic trunks, for instance, to orient and control themselves automatically, to pick things up and interact with the world.

The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions. The Digital Clock Drawing Test (dCDT) uses novel software to analyze data from a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making possible the analysis of both the drawing process and final product. We developed methodology to analyze pen stroke data from these drawings, and computed a large collection of features which were then analyzed with a variety of machine learning techniques. The resulting scoring systems were designed to be more accurate than the systems currently used by clinicians, but just as interpretable and easy to use. The systems also allow us to quantify the tradeoff between accuracy and interpretability. We created automated versions of the CDT scoring systems currently used by clinicians, allowing us to benchmark our models, which indicated that our machine learning models substantially outperformed the existing scoring systems.