Author Archives: Marc Mößer (Moesser)

From code to molecules: The future of chemical synthesis

In June, after I finish my PhD, I will be joining Chemify, a new startup based in Glasgow that aims to make chemical synthesis universally accessible, reproducible and fully automated using AI and robotics. After previously talking about “Why you should care about startups as a researcher” and a quick guide on “Commercialising your research: Where to start?” on this blog, I have now joined a science-based startup fresh out of university myself.

Chemify is a spinout from the University of Glasgow originating from the group of Prof. Lee Cronin. The core of the technology is the chemical programming language χDL (pronounced “chi DL”) that, in combination with a natural language processing AI that reads and understands chemical synthesis procedures, can be used to plan and autonomously executed chemical reactions on robotic hardware. The Cronin group has also already build the modular robotic hardware needed to carry out almost any chemical reaction, the “Chemputer”. Due to the flexibility of both the Chemputer and the χDL language, Chemify has already shown that the applications go way beyond simple synthesis and can be applied to drug formulation, the discovery of new materials or the optimisation of reaction conditions.

Armed with this transformational software and hardware, Chemify is now fully operational and is hiring exceptional talent into their labs in Glasgow. I am excited to see how smart, AI-driven automation techniques like Chemify will change how small scale chemical synthesis and chemical discovery more broadly is done in the future. I’m super excited to be part of the journey.

Post-processing for molecular docking: Assigning the correct bond order using RDKit.

AutoDock4 and AutoDock Vina are the most commonly used open-source software for protein-ligand docking. However, they both rely on a derivative of the “PDB” (Protein Data Base) file format: the “PDBQT” file (Protein Data Bank, Partial Charge (Q), & Atom Type (T)). In addition to the information contained in normal PDB files, PDBQT files have an additional column that lists the partial charge (Q) and the assigned AutoDock atom type (T) for each atom in the molecule. AutoDock atom types offer a more granular differentiation between atoms such as listing aliphatic carbons and aromatic carbons as separate AutoDock atom types.

The biggest drawback about the PDBQT format is that it does not encode for the bond order in molecules explicitly. Instead, the bond order is inferred based on the atom type, distance and angle to nearby atoms in the molecule. For normal sp3 carbons and molecules with mostly single bonds this system works fine, however, for more complex structures containing for example aromatic rings, conjugated systems and hypervalent atoms such as sulphur, the bond order is often not displayed correctly. This leads to issues downstream in the screening pipeline when molecules suddenly change their bond order or have to be discarded after docking because of impossible bond orders.

The solution to this problem is included in RDKit: The AssignBondOrdersFromTemplate function. All you need to do is load the original molecule used for docking as a template molecule and the docked pose PDBQT file into RDKIT as a PDB, without the bond order information. Then assign the original bond order from your template molecule. The following code snippet covers the necessary functions and should help you build a more accurate and reproducible protein-ligand docking pipeline:

#import RDKit AllChem
from rdkit import Chem
from rdkit.Chem import AllChem


#load original molecule from smiles
SMILES_STRING = "CCCCCCCCC" #the smiles string of your ligand
template = Chem.MolFromSmiles(SMILES_STRING)

#load the docked pose as a PDB file
loc_of_docked_pose = "docked_pose_mol.pdb" #file location of the docked pose converted to PDB file
docked_pose = AllChem.MolFromPDBFile(loc_of_docked_pose)

#Assign the bond order to force correct valence
newMol = AllChem.AssignBondOrdersFromTemplate(template, docked_pose)

#Add Hydrogens if desired. "addCoords = True" makes sure the hydrogens are added in 3D. This does not take pH/pKa into account. 
newMol_H = Chem.AddHs(newMol, addCoords=True)

#save your new correct molecule as a sdf file that encodes for bond orders correctly
output_loc = "docked_pose_assigned_bond_order.sdf" #output file name
Chem.MolToMolFile(newMol_H, output_loc)

Using normalized SuCOS scores.

If you are working in cheminformatics or utilise protein-ligand docking, then you should be aware of the SuCOS score, an open-source shape and chemical feature overlap metric designed by a former member of OPIG: Susan Leung.

The metric compares the 3D conformers of two ligands based on their shape overlap as well as their chemical feature overlap using the RDKit toolkit. Leung et al. show that SuCOS is able to select fewer false positives and false negatives when doing re-docking studies than other scoring metrics such as RMSD or Protein Ligand Interaction Fingerprints (PLIF) similarity scores and performs better at differentiating actives from decoys when tested on the DUD-E dataset.

Most importantly, SuCOS was designed with fragment based drug discovery in focus, where a smaller fragment ligand is elaborated or combined with other fragments to create a larger molecule, with hopefully stronger binding affinity. Unlike for example RMSD, SuCOS is able to quickly calculate an overlap score between a small fragment and a larger molecule, giving chemists an idea on how the fragment elaboration might interact with the protein. However, the original SuCOS algorithm was not normalized and could create scores of > 1 for some cases.

I’ve uploaded a normalised version of the original SuCOS algorithm as a GitHub fork of Susan’s original repository. You can find the normalised SuCOS algorithm here.

Hopefully this is helpful for anyone using the SuCOS algorithm and for all docking enthusiasts who are interested in an alternative way to evaluate their docked poses.

Watch out when using PDBbind!

Now that PDBbind 2020 has been released, I want to draw some attention to an issue with using the SDF files that are supplied in the PDBbind refined set 2020.

Normally, SDF files save the chirality information of compounds in the atom block of the file which is shown belowas a snipped of the full sdf file for the ligand of PDB entry 4qsv. The column that defines chirality is marked in red.

As you can see, all columns shown here are 0. The SDF files supplied by PDBbind for some reason do NOT encode chirality information explicitly. This will be a problem when using RDKit to read the molecule and transform it into a smiles string. By using the following commands to read the ligand for 4qsv from PDBBind 2020 and write a SMILES string, we get:

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Commercialising your research: Where to start?

If you look at some of the biggest technology companies in the world, from Google and Facebook to hardware companies like Dell or even biotech unicorns like Oxford’s own Oxford Nanopore, all of them started on university campuses. If you are a researcher interested in finding out how to make the first steps to commercialise your research here is a quick guide:

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Drug Promiscuity vs Selectivity

In drug discovery, compound promiscuity and selectivity refers to the ability of drug compounds to bind to several different- (promiscuous) or only one main target (selective). An important distinction here is that promiscuity is defined as specific interactions with multiple biological targets (polypharmacology) rather than a number of non-specific targets. At first glance, you might expect drugs to be designed to be as selective as possible, only hitting one biological target necessary to treat the disease and therefore reduce the chance of any side effects. This paradigm of single-target specificity has been challenged over the past two decades. Even between scientists in the drug discovery field, compound promiscuity is still a controversial topic. The field has increasingly paid attention to the topic of polypharmacology and studies have shown many pharmaceutically relevant compounds, including approved drugs to derive their biological activity from polypharmacology [1-3].

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State of the art in AI for drug discovery: more wet-lab please

The reception of ML approaches for the drug discovery pipeline, especially when focused on the hit to lead optimization process, has been rather skeptical by the medchem community. One of the main drivers for that is the way many ML publications benchmark their models: Historic datasets are split into two parts, with the larger part used to train and the smaller to test ML models. In order to standardize that validation process, computational chemists have constructed widely used benchmark datasets such as the DUD-E set, which is commonly used as a standard for protein-ligand binding classification tasks. Common criticism from medicinal chemists centers on the main problem associated with benchmark datasets: the absence of direct lab validation.

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Why you should care about startups as a researcher

I was recently awarded the EIT Health Translational Fellowship, which aims to fund DPhil projects with the goal of commercializing the research and addressing the funding gap between research and seed funding. In order to win, I had to deliver a short 5 minute startup pitch in front of a panel of investors and scientific experts to convince them that my DPhil project has impact as well as commercial viability. Besides the £5000 price, the fellowship included a week-long training course on how to improve your pitch, address pain points in your business strategy etc. I found the whole experience to be incredibly rewarding and the skills I picked up very important, even as a researcher. As a summary, this is why I think you should care about the startup world as a researcher.

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Chat bots and the Turing test

When I recently tested out the voice activation features on my phone, I was extremely impressed with how well it understood not only the actual words I was saying, but also the context. The last time I used voice control features was years ago when the technology was still in its infancy. There was only a specific set of commands the voice recognition software understood and most of them were hard-coded. Given the impressive advances we have made utilizing machine learning for voice recognition and natural language processing to the point where I can tell my phone: “Hey Google, can you give me a list of the best BBQ restaurants near me?” and it will actually understand and do it, it is interesting that we still struggle with a language based technology that has been around for ages: chatbots.

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