Artificial Intelligence (AI) and Machine Learning (ML) are weaving their way into the fabric of society, where they are playing a crucial role in numerous facets of our lives. As we witness the increased deployment of AI and ML in various types of devices, we benefit from their use into energy-efficient algorithms for low powered devices. In this paper, we investigate a scale and medium that is far smaller than conventional devices as we move towards molecular systems that can be utilized to perform machine learning functions, i.e., Molecular Machine Learning (MML). Fundamental to the operation of MML is the transport, processing, and interpretation of information propagated by molecules through chemical reactions. We begin by reviewing the current approaches that have been developed for MML, before we move towards potential new directions that rely on gene regulatory networks inside biological organisms as well as their population interactions to create neural networks. We then investigate mechanisms for training machine learning structures in biological cells based on calcium signaling and demonstrate their application to build an Analog to Digital Converter (ADC). Lastly, we look at potential future directions as well as challenges that this area could solve.

The intersection of machine learning with chemistry has been going on for decades. In recent years, with the advent of sophisticated deep learning methods, machine learning in molecular studies has garnered interest from the scientists' community. Molecular machine learning has seen tremendous growth in recent years and has also seen an increase in predictions about molecular properties. By applying machine learning, researchers have now changed the way creativity is being considered, with artificial intelligence can now able to create original images, music and text. With such an advancement in other industries, researchers have now applied machine learning in molecular studies which are popularly known as molecular machine learning.

Chemical fingerprints [1] have long been the representation used to represent chemical structures as numbers, which are suitable inputs to machine learning models. A brief summary of chemical fingerprints is provided in another of my blog posts here. Above, we computed the fingerprint for Atorvastatin, a drug which generated over $100B in revenue over 2003–2013. At some point a few years ago, people started to realize [3] that instead of computing a non-differentiable fingerprint, we can compute a differentiable fingerprint. Then, by backpropagation, we can train not only a deep-learning model but also train the fingerprint-generating function itself. The promise would be to learn richer molecular representations.