Accelerated molecular discovery

Combining physical, biological and data sciences
for insight and molecular discovery

Algorithmic designs to predict new classes of antimicrobials

Infections will be the deadliest diseases unless we act now. Up to 700,000 people die every year of drug-resistant infections, increasing to 10 million by 2050 and costing the economy a total of 100 trillion USD. To avoid “being cast back into the dark ages of medicine,” there is an urgent need backed by UK government strategy to conserve existing antibiotics and discover new ones.

Combining techniques at the interface between data, physical and biological sciences is a powerful tool to design new molecules and materials. Biomolecular simulation gives a detailed understanding of how molecules interact with targets, and data science can find patterns and design rules in large datasets. Together with biophysical experiments, these techniques create a feedback cycle that leads to quick insight and accelerated discovery.

In this context, we are exploring the rational design of new classes of antimicrobials. Our initial target is to mine protein sequences for short antimicrobial motifs that kill bacteria, but leave human cells unaffected. We seek to demonstrate how experimental and computer-generated datasets can be harvested to reveal features that inform new design concepts. We are developing new analysis and visualisation tools. We are revealing new molecular modes of action by which proteins interact and permeate cell membranes. The aim is to design new antimicrobial compounds unrelated to others.

Discovery loop

Next-generation materials modelling at the atomic and molecular scale

Despite steady advances in computational power, tradeoffs exist between completeness of the description of interactions and the time and length scales that are accessible for sampling. Thus, simulation of complex systems is achieved using highly simplified models, which may not capture the fundamental interactions responsible for emergent behavior. This is particularly acute in (but not unique to) bimolecular simulation where the pharmaceutical industry is seeking new innovations in primary methods.

We have developed a fundamentally new strategy for efficient, predictive models at the molecular scale with complete electronic responses, reduced reliance on empirical input from the condensed phase and the prospects for vastly improved transferability and predictive value. The resulting level of completeness in physical description enables isolated molecule properties to define model parameters, thereby eliminating fitting to condensed phase data. Thus, the framework provides a physical and intuitive basis for predictive, next-generation simulation.

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Glenn Martyna

Glenn Martyna
IBM T.J. Watson Research Center