Monthly Archives: August 2018

Mol2vec: Finding Chemical Meaning in 300 Dimensions

Embeddings of Amino Acids

2D projections (t-SNE) of Mol2vec vectors of amino acids (bold arrows). These vectors were obtained by summing the vectors of the Morgan substructures (small arrows) present in the respective molecules (amino acids in the present example). The directions of the vectors provide a visual representation of similarities. Magnitudes reflect importance, i.e. more meaningful words. [Figure from Ref. 1]

Natural Language Processing (NLP) algorithms are usually used for analyzing human communication, often in the form of textual information such as scientific papers and Tweets. One aspect, coming up with a representation that clusters words with similar meanings, has been achieved very successfully with the word2vec approach. This involves training a shallow, two-layer artificial neural network on a very large body of words and sentences — the so-called corpus — to generate “embeddings” of the constituent words into a high-dimensional space. By computing the vector from “woman” to “queen”, and adding it to the position of “man” in this high-dimensional space, the answer, “king”, can be found.

A recent publication of one of my former InhibOx-colleagues, Simone Fulle, and her co-workers, Sabrina Jaeger and Samo Turk, shows how we can embed molecular substructures and chemical compounds into a similarly high-dimensional, continuous vectorial representation, which they dubbed “mol2vec“.1 They also released a Python implementation, available on Samo Turk’s GitHub repository.

 

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Cinder: Crystallographic Tinder

Protein structure determination is still dominated by xray diffraction. For diffraction studies structural biologists need to grow and optimise protein crystals until they diffract to an usable and optimal resolution. A purified protein sample is exposed to a number of crystallisation screens, each comprising a selection of chemical conditions that are designed to explore a reasonably wide area of potential crystallisation conditions.

Many crystallography labs routinely image these in large plate storage systems, which reduces the human interaction to viewing a set of usually 100-1000 images at various time points. This is a slow and laborious process, and highly applicable to machine learning approaches tailored to looking at images. TexRank, a texton analysis ranking software was developed by Jia Tsing in OPIG and is used at the Structural Genomics Consortium (SGC). This ranking reduces the number of images that a human needs to search through, providing a quicker review process.  Continue reading →