Collaborating Authors

Machine learning uncovers potential new TB drugs


Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs. MIT researchers have now incorporated a new feature into these types of machine-learning algorithms, improving their prediction-making ability. Using this new approach, which allows computer models to account for uncertainty in the data they're analyzing, the MIT team identified several promising compounds that target a protein required by the bacteria that cause tuberculosis. This method, which has previously been used by computer scientists but has not taken off in biology, could also prove useful in protein design and many other fields of biology, says Bonnie Berger, the Simons Professor of Mathematics and head of the Computation and Biology group in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "This technique is part of a known subfield of machine learning, but people have not brought it to biology," Berger says.

Global Big Data Conference


How is Machine Learning helping to develop TB drugs? Many biologists use machine learning (ML) as a computational tool to analyze a massive amount of data, helping them to recognise potential new drugs. MIT researchers have now integrated a new feature into these types of machine learning algorithms, enhancing their prediction-making ability. Using this new tool allows computer models to account for uncertainty in the data they are testing, MIT researchers detected several promising components that target a protein required by the bacteria that cause tuberculosis (TB). Although computer scientists previously used this technique, they have not taken off in biology.

Finding unique drug structures with artificial intelligence and chemistry


In the search for new medicines for diseases such as cancer, a Leiden team has developed a new workflow. This approach combines artificial intelligence (AI) with molecular modelling and is suitable for finding unknown and innovative drug structures, the researchers demonstrated. With their new method, the researchers of the Leiden Academic Centre for Drug Research (LACDR) and the Leiden Institute of Advanced Computer Science (LIACS) were able to find five substances with an inhibitory effect on a specific type of kinase. Kinases are enzymes that switch other proteins on or off and play an important role in the development of cancer. In their publication in the Journal of Chemical Information and Modeling, the team looked at so-called polypharmacology – drug development in which there are multiple targets in the body.

WideDTA: prediction of drug-target binding affinity Machine Learning

Motivation: Prediction of the interaction affinity between proteins and compounds is a major challenge in the drug discovery process. WideDTA is a deep-learning based prediction model that employs chemical and biological textual sequence information to predict binding affinity. Results: WideDTA uses four text-based information sources, namely the protein sequence, ligand SMILES, protein domains and motifs, and maximum common substructure words to predict binding affinity. WideDTA outperformed one of the state of the art deep learning methods for drug-target binding affinity prediction, DeepDTA on the KIBA dataset with a statistical significance. This indicates that the word-based sequence representation adapted by WideDTA is a promising alternative to the character-based sequence representation approach in deep learning models for binding affinity prediction, such as the one used in DeepDTA. In addition, the results showed that, given the protein sequence and ligand SMILES, the inclusion of protein domain and motif information as well as ligand maximum common substructure words do not provide additional useful information for the deep learning model. Interestingly, however, using only domain and motif information to represent proteins achieved similar performance to using the full protein sequence, suggesting that important binding relevant information is contained within the protein motifs and domains.

Artificial Intelligence Model Identifies 'Amazing' Antibiotic Candidate


Researchers at Massachusetts Institute of Technology (MIT) have harnessed a machine-learning algorithm to identify a new antibiotic compound that, in laboratory tests, killed many of the world's most challenging disease-causing bacteria, including some strains that are resistant to all known antibiotics. The new antibiotic candidate, which has been given the name halicin--after the fictional artificial intelligence system from "2001: A Space Odyssey,"--was discovered in the Drug Repurposing Hub, and is structurally different to conventional antibiotics. Initial in vivo experiments showed that halicin was effective against Clostridium difficile and pan-resistant Acinetobacter baumannii infections in two mouse models. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," said James Collins, PhD, the Termeer professor of medical engineering and science in MIT's Institute for Medical Engineering and Science (IMES) and department of biological engineering. "Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered."