This is a blog post about using fewer words.

Start with incomplete sentences. Without qualifiers or unnecessary adjectives. Repeated words are discarded. Logical ideas are tied together after the key points are down.

Use no or stronger adjectives. Many words are simply unnecessary or we can combine them:

This terrible, poorly rationalised, ad hoc approach does not greatly contribute to the growing field.

Could be written as: This naive approach fails to benefit the field.

When we use fewer words, the ideas speak for themselves. The logical clarity is enhanced and, often, it’s pleasant to read. If you do add adjectives or qualifiers, they should make the style sparkle. 

Write aloud. The tone will be friendlier and can identify the points you are trying to communicate.

Ablate redundancy and repetition. Need I say more?

Right leaning can be avoided. This is a little bit more complex. We’ll often find, when we are writing sentences, that it takes a long time to get to the end of it. The last sentence is an example of right lean. Putting the meat at the beginning can clarify and shorten a sentence. For example: The reason that our method was unable to handle this particular scenario was because it makes the assumption of homoscedasticity. Could become: When confronted with heteroscedasticity, our method performs poorly. 

Avoid phrase conjunctions. Phrases such as: “It is necessary that” and “in light of” are clunky.

Fewer negative sentences can add clarity. For example: This method does not perform well, unless you choose these parameters settings and also do not use a window machine. Could become: Mac or Linux users, who use these parameter settings, will have superior performance. 

Rethink the structure. Correctly grouping the main ideas will remove the unneeded text. 

Here is the AlphaFold paper abstract:

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort, the structures of around 100,000 unique proteins have been determined, but this represents a small fraction of the billions of known protein sequences. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’—has been an important open research problem for more than 50 years. Despite recent progress, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14), demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

Let’s rewrite it, but I’ll avoid changing the meaning or essence.

Proteins are essential and their mechanistic function can be established from their structure. Decades of experiments have determined around 100,000 unique protein structures. However, this represents only part of the billions of known protein sequences. It can take years to determine a single protein structure and so computational methods can enable protein studies. Predicting the three-dimensional structure of a protein from solely its amino acid sequence has been an open research problem for several decades. When homologous structures are available current methods perform well, otherwise the atomic accuracy drops. We present the first computational method that can predict protein structures at atomic accuracy in these cases. Our approach overhauls our previous neural network-based model AlphaFold and we validate it in the challenging 14th Critical Assessment of Protein Structure Prediction (CASP14). Our superior method demonstrates accuracy competitive with experimental structures. The approach rests on a novel idea that incorporates physical and biological knowledge of protein structure and leverages multi-sequence alignments.