Drug Binding Affinity Predictions to Achieve Targeted Drug Discovery

The discovery of new drugs is tightly connected to a detailed understanding of drug binding sites and overall drug-substrate interaction mechanisms. To this end we have been using the concept of free energy mapping, which allow for the reconnaissance and weighting of all possible ways a drug can bind to a substrate. The use of techniques of molecular dynamics is in this respect critical, as it allows for simulations at thermodynamic conditions, which corresponds to experimental/physiological values. Since the investigation of activated processes introduces an intrinsic inefficiency in standard methods of molecular dynamics, advanced techniques will be used, including metadynamics, transition path sampling, and the newly implemented, proprietary method metashooting.
An efficient molecular dynamics simulation relies on an efficient calculation of interatomic forces. For biological systems this has been guaranteed by empirical force-fields, since the size of relevant systems excludes the use of any ab initio, density functional based techniques, which are simply computationally too heavy for any practical application in this area.
Among the target systems, we are focusing on DNA tetramers, so called G4s, which have been studied more frequently in recent years due to their role in cancer cells. When G4s form at the telomere end of a strand of DNA, telomerase cannot access the site to extend the DNA sequence. In cancer cells the telomerase enzyme will normally extend the DNA sequence uninterrupted, which leads to cell immortalisation. By preventing this immortalisation cancer cells cannot replicate indefinitely, slowing the growth of a tumour. G4's are stabilised by metal ions, as also demonstrated in previous work focusing on Gold/NHC compounds using metadynamics. The latter has proved the efficiency of the molecular dynamics approach, but has also indicated limitations in the way energies and forces are calculated.
In this thesis we intend to reach a superior level of efficiency and accuracy by combining technique of advanced molecular dynamics with machine-learned force fields for organic/biological systems. Machine learning is providing a more efficient, portable way to map potential energy surfaces, in a way that a higher level of theory can be "learned" and subsequently expressed on a computationally cheaper time scale, which allows for investigations previously very impractical or even impossible. The use of machine-learned potentials will allow to study activated processes in biological systems (bond breaking, catalytic steps, proton transfer processes) at computational costs, comparable to force fields. This will open completely new perspectives in the study of drug action in biological systems, and on the energetics of activated steps.
Besides original investigations on metallodrug-substrate interactions and energetics, benchmarks will be performed, between all force-field simulations, QM/MM approaches and machine-learned potentials, for comparison and further optimisation of the latter method.