An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials.

The Open Source Malaria (OSM) consortium is developing compounds that kill the human malaria parasite, Plasmodium falciparum, by targeting PfATP4, an essential ion pump on the parasite surface. The structure of PfATP4 has not been determined. Here, we describe a public competition created to develop a predictive model for the identification of PfATP4 inhibitors, thereby reducing project costs associated with the synthesis of inactive compounds. Competition participants could see all entries as they were submitted. In the final round, featuring private sector entrants specializing in machine learning methods, the best-performing models were used to predict novel inhibitors, of which several were synthesized and evaluated against the parasite. Half possessed biological activity, with one featuring a motif that the human chemists familiar with this series would have dismissed as "ill-advised". Since all data and participant interactions remain in the public domain, this research project "lives" and may be improved by others

Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules.

We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications to bond dissociation energy (BDE) prediction, for example, in the context of cytochrome P450 (CYP) metabolism. The transferability of the MACE potential was validated on the COMP6 data set, containing only closed-shell molecules, where it reaches better accuracy than the readily available general ANI-2x potential. MACE achieves similar accuracy on two CYP metabolism-specific data sets, which include open- and closed-shell structures. This model enables us to calculate the aliphatic C-H BDE, which allows us to compare reaction energies of hydrogen abstraction, which is the rate-limiting step of the aliphatic hydroxylation reaction catalyzed by CYPs. On the "CYP 3A4" data set, MACE achieves a BDE RMSE of 1.37 kcal/mol and better prediction of BDE ranks than alternatives: the semiempirical AM1 and GFN2-xTB methods and the ALFABET model that directly predicts bond dissociation enthalpies. Finally, we highlight the smoothness of the MACE potential over paths of sp3C-H bond elongation and show that a minimal extension is enough for the MACE model to start finding reasonable minimum energy paths of methoxy radical-mediated hydrogen abstraction. Altogether, this work lays the ground for further extensions of scope in terms of chemical elements, (CYP-mediated) reaction classes and modeling the full reaction paths, not only BDEs

Dynamic Chiral Cyclohexanohemicucurbit[12]uril

This research with title "Dynamic chiral cyclohexanohemicucurbit[12]uril" is dedicated to the memory of late Professor Hans J. Reich.Abstract:NMR and DFT studies of chiral cyclohexanohemicucurbit[12]uril indicate that the macrocycle adopts a concave octagon shape with three distinct ranges of conformational flexibility in solution. Methylene bridge flipping occurs at temperatures above 265 K, while urea monomers rotate at temperatures above 308 K resulting in the loss of confined space within the macrocycle

Chiral hemicucurbit[8]uril as an anion receptor: selectivity to size, shape and charge distribution

A novel eight-membered macrocycle of the hemicucurbit[n]uril family, chiral (all-R)-cyclohexanohemicucurbit[8]uril (cycHC[8]) binds anions in a purely protic solvent with remarkable selectivity. The cycHC[8] portals open and close to fully encapsulate anions in a 1 : 1 ratio, resembling a molecular Pac-Man™. Comprehensive gas, solution and solid phase studies prove that the binding is governed by the size, shape and charge distribution of the bound anion. Gas phase studies show an order of SbF6− ≈ PF6− > ReO4− > ClO4− > SCN− > BF4− > HSO4− > CF3SO3− for anion complexation strength. An extensive crystallographic study reveals the preferred orientations of the anions within the octahedral cavity of cycHC[8] and highlights the importance of the size- and shape-matching between the anion and the receptor cavity. The solution studies show the strongest binding of the ideally fitting SbF6− anion, with an association constant of 2.5 × 105 M−1 in pure methanol. The symmetric, receptor cavity-matching charge distribution of the anions results in drastically stronger binding than in the case of anions with asymmetric charge distribution. Isothermal titration calorimetry (ITC) reveals the complexation to be exothermic and enthalpy-driven. The DFT calculations and VT-NMR studies confirmed that the complexation proceeds through a pre-complex formation while the exchange of methanol solvent with the anion is the rate-limiting step. The octameric cycHC[8] offers a unique example of template-controlled design of an electroneutral host for binding large anions in a competitive polar solvent.peerReviewe

An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials

The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the problem of increasing resistance to our frontline treatments. The Open Source Malaria (OSM) consortium has been developing compounds ("Series 4") that have potent activity against Plasmodium falciparum in vitro and in vivo and that have been suggested to act through the inhibition of PfATP4, an essential membrane ion pump that regulates the parasite’s intracellular Na+ concentration. The structure of PfATP4 is yet to be determined. In the absence of structural information about this target, a public competition was created to develop a model that would allow the prediction of anti-PfATP4 activity among Series 4 compounds, thereby reducing project costs associated with the unnecessary synthesis of inactive compounds.In the first round, in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably, all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition was undertaken, in 2019, again with freely-donated models that other participants could see. The best-performing models from this second round were used to predict novel inhibitory molecules, of which several were synthesised and evaluated against the parasite. One such compound, containing a motif that the human chemists familiar with this series would have dismissed as ill-advised, was active. The project demonstrated the abilities of new machine learning methods in the prediction of active compounds where there is no biological target structure, frequently the central problem in phenotypic drug discovery. Since all data and participant interactions remain in the public domain, this research project “lives” and may be improved by others