Poster Abstracts
De Novo Design of GALK1 Inhibitors in a Flexible Binding Pocket
Speaker: Asma Feriel Khoualdi
Additional Authors: Daniel Cole, Matteo Degiacomi and Wyatt Yue
Institution: Newcastle University
Abstract:
Galactosemia is a rare autosomal recessive disease caused by a defect in galactose metabolism. GALK1 is a kinase enzyme involved in galactose metabolism and whose inhibition can help alleviate galactosemia. By making use of the GALK1’s allosteric inhibitors which are crystalized, molecules with better binding properties can be designed, modified and optimized within the binding pocket using FEgrow tool. Molecules can be designed interactively or automatically using substructures from databases, built and scored using FEgrow. Pocket flexibility is also addressed with the conformation prediction tool Molearn which uses a convolutional neural network to learn from relatively short, example MD simulation trajectories to predict the different conformations the protein will have.
Estimation of the maximum IQA energy error for source-to-target predictions in oligopeptide and polyacetylene systems
Speaker: Lisan David Cabrera Gonzalez
Additional Authors: Prof. Paul Popelier
Institution: University of Manchester
Abstract:
The atomic IQA energy in several target systems, such as Alanine-Alanine-Alanine (C12N4O4H22), Gycine-Glycine-Glycine (C9N4O4H16), and Polyacetylene [C2H2]12, is predicted using smaller source systems. A neural network is trained to estimate the maximum IQA energy error for each source-to-target system. The input features of the neural network are functions of the source atomic charges and the source and target atomic elements and configurations. Therefore, this allows us to estimate the maximum error for source-to-target predictions of the IQA energy of the target system with only the atomic charges of the source system, the elements of the atoms, and their geometrical configuration. Such estimation will allow us to decide if a source system is adequate to predict the IQA energy of a larger target system without making further calculations, except for the atomic charge.
Neural Network Potentials for pKa Prediction
Speaker: Ross Urquhart
Additional Authors: Alexander van Teijlingen, Tell Tuttle
Institution: University of Strathclyde
Abstract:
Measurement of acidity/basicity (pKa) is crucial to many disciplines of chemistry including pharmacology, drug discovery and theoretical chemistry. Traditionally, methods like titration, NMR and spectroscopy are used, but they are costly and time consuming. Theoretical techniques such as DFT and recently machine learning (ML) is increasingly being used to predict pKa values much faster and at lower cost.
Machine learning potentials blend empirical and high-level methods like DFT, offering a balance between computational speed and accuracy. Specifically, neural network potentials (NNPs) excel in converting molecular structures into Potential Energy Surfaces for swift and precise energy forecasts. ANAKIN-ME (ANI),1 a leading NNP, achieves sub-kcal/mol precision on test datasets and was the first NNP to show transferability to systems beyond its initial training.
We show for the first time that ANI-architecture NNPs can be extended to different phases and charge states which we use to calculate the pKa of imidazole’s with a pKa range of 22-34. Using thermodynamic cycles to calculate the free energy change of the aqueous phase requires different models to work in tandem with each other to perform the calculation, a process that is very sensitive to minor changes in energy, which has led to traditionally high errors in pKa calculation. Twinned with low-energy conformer searches via CREST,2 our method allows for molecules to be modelled beyond the global minima, providing a more realistic model of the molecules in solution and, which gives a higher prediction accuracy of pKa with a RMSE value of 2.02 pKa units.
References:
(1) Smith, J. S.; Isayev, O.; Roitberg, A. E., Chem Sci 2017, 8 (4), 3192-3203
(2) Pracht, P.; Bohle, F.; Grimme, S., Phys Chem Chem Phys 2020, 22 (14), 7169-7192
Machine Learning Excited State Potential Energy Surfaces of Solvated Nile Red with ESTEEM
Speaker: Jacob Eller
Additional Authors: Prof. Nicholas Hine
Institution: University of Warwick
Abstract:
Machine Learned Interatomic Potentials (MLIPs) offer a powerful combination of abilities for accelerating theoretical spectroscopy calculations utilising both ensemble sampling and trajectory post-processing for inclusion of vibronic effects, which can be very challenging for traditional ab initio MD approaches. We demonstrate a workflow that enables efficient generation of MLIPs for the solvatochromic dye nile red system, in a variety of solvents. We use iterative active learning techniques to make this process as efficient as possible in terms of number and size of DFT calculations. Additionally, we compare the efficacy of two methodologies: generating distinct MLIPs for each adiabatic state, and using one ground state MLIP in combination with delta-ML of excitation energies. To evaluate the validity of the resulting models, we compare predicted absorption and emission spectra to experimental spectra.
Automating Transition State Search in Metal Catalysed Reactions
Speaker: Shoubhik Raj Maiti
Additional Authors: Fernanda Duarte, David Buttar
Institution: University of Oxford
Abstract:
Finding transition states (TS) is a key step in elucidating the mechanisms underlying chemical reactions, facilitating the optimisation of synthetic procedures and the discovery of new catalysts. Traditional approaches to finding TSs employing DFT or similar methods have become routine. However, despite advances in the field, characterising TSs still requires significant human time and effort. Automation of TS search has the potential to address these challenges. Indeed, several advances have been made in this area using molecular graph-based methods,[1,2] or by systematic exploration of the potential energy surface (PES)[3]; however, most of them have focused on organic reactions, and struggle to describe transition metal (TM) catalysed reactions, especially transition states and intermediate states. This is due to the complex potential energy surfaces (PES) of these systems, which arises from their complex electronic structure and flexible coordination ability. This also makes constructing molecular graphs challenging, consequently making it difficult to automate TS search for TM-catalysed reactions. Given the relevance of these reactions in pharmaceutical and materials industry, it is clear that automated in silico elucidation of reaction paths and their kinetics holds promise for optimising existing catalysts and designing new ones.
In this study, we present our efforts to design an automated workflow for TS search and reaction path elucidation for TM- catalysed reactions, building on our software autodE[2]. We discuss our implementation of recently published double-ended TS search method i-EIP (improved Elastic Image Pair)[4] in autodE. We then compare its robustness and efficiency against popular double-ended methods, including NEB-TS (Nudged Elastic Band – Transition State),[5] DE-GSM (Double-Ended Growing String Method)[6] and DHS (Dewar-Healy-Stewart)[7] across a series of TM-catalysed reactions. The results indicate that popular methods may not always be the most efficient. Additionally, we introduce a fast method of generating molecular graphs for metal complexes from low-level tight-binding calculations, which can improve the reliability of graph-based representations of reactions as used in autodE. We aim for this study to contribute to the broader application of automated reaction path-finding methods, paving the way for faster development of more efficient and selective catalysts.
References:
[1] L. D. Jacobson, A. D. Bochevarov et al., J. Chem. Theory Comput. 2017, 13, 5780
[2] T. A. Young, J. J. Silcock, A. J. Sterling, F. Duarte, Angew. Chem. Int. Ed. 2021, 60, 4266
[3] S. Maeda, K. Morokuma, J. Chem. Phys. 2010, 132, 241102
[4] Y. Liu, H. Qi, M. Lei, J. Chem. Theory Comput. 2023, 19, 2410
[5] A. Asgeirsson, H. Jonsson, et al., J. Chem. Theory. Comput. 2021, 17, 4929-4945
[6] P. Zimmerman, J. Chem. Phys. 2013, 138, 184102
[7] M. J. S. Dewar, E. F. Healy, J. J. P. Stewart, J. Chem. Soc., Faraday Trans. 2 1984, 80, 227