Aspartic proteases are wide spread in eukaryotes, and they are attracting attention as drug targets . This family of enzymes uses an activated water molecule bound to the catalytic dyad -two aspartic acid residues -to attack the scissile peptide bond in a substrate. The aspartic protease catalysed peptide bond cleavage can be prevented by inhibitors that replace this catalytic water molecule or in which the scissile peptide bond is replaced by a non-cleavable moiety. There are numerous inhibitors reported in literature targeting aspartic proteases involved in HIV/AIDS, malaria, Alzheimer's disease, etc . Over the recent years, aspartic proteases from the malaria parasite -plasmepsins (plms) -have gained particular interest from medicinal chemists due to increasing resistance of known antimalarials . An important issue when developing pathogen aspartic protease inhibitors is their selectivity over human enzymes. Plms share high sequence similarity with several human aspartic proteases, the most similar being the lysosomal enzyme cathepsin D (catD). Therefore, it is commonly used as a marker for cross-inhibition. Plm II and catD share 35% sequence identity, and even higher similarity is observed in the active site. Due to the high protein structural similarity, design of potent plasmepsin inhibitors that do not inhibit catD remains a challenging task. Recent studies have shown that plm inhibitor selectivity over human aspartic proteases can be achieved by targeting the flap loop region of plms. Flap loop is a single long β-hairpin structure that lies perpendicularly over the aspartic dyad (see Figure .A), and is believed to be involved in the substrate recognition . There have been numerous, mostly computational, studies focused on the characterisation of aspartic protease flap loop dynamics . It is commonly acknowledged , that the flap-loop of aspartic proteases occupies a semi-open conformation in apo state. However, the hinge like motion of the flap loop is also capable of exposing a hydrophobic pocket (see Figure .B) under the flap loop producing so called open-flap conformation . Inhibitors that bind partly under the flap loop (also known as open-flap inhibitors) have improved selectivity over human aspartic proteases, and the selectivity gain is attributed to differences in enzyme flexibility -plms easily reach openflap conformation where an additional hydrophobic pocket is formed, whereas cathepsins either do not have or do not easily reach an open-flap conformation. The precise mechanism of enzyme reorganisation for the inhibitor binding to open-flap conformation, however, is not yet known. It is unclear what are the protein structural features and key protein-inhibitor interactions that enable flap loop opening, and lead to inhibitor binding under the flap loop. Such information would allow us to design inhibitors with improved selectivity not only against plms, but other aspartic proteases, and, possibly, other enzymes with highly mobile loops near the binding site. An accurate description of the flap loop motion upon ligand binding at a molecular scale is essential to understand selectivity determining factors of open-flap inhibitors. Currently, however, there are no experimental methods that would enable such dynamic process characterisation at a molecular scale. Lately, in-silico techniques have emerged as a viable alternative to experimental methods and are becoming increasingly effective in providing atomic-level description of protein-ligand interactions . Dynamic processes like these are typically studied using molecular dynamics (MD) simulations, however, ligand binding/unbinding is a rare event that can be hard to sample within a reasonable time . Therefore, enhanced sampling methods like metadynamics (MetaD) are utilised to accelerate the occurrence of rare events by depositing a history dependent biasing potential to selected degrees of freedom (also known as collective variables (CVs)) . The bias potential within CV sub-space is built up by adding Gaussians along the sampled trajectory, and bias potential applied encourages the system to visit higher energy states and discourage the system from revisiting configurations already sampled. This approach leads to a significantly improved exploration of the simulation space, and, once the exploration is complete, an efficient reconstruction of the free energy surface (FES) as a function of the CVs used . MetaD in its various modifications has been applied in numerous ligand-protein binding studies . One of these MetaD variations is funnel metadynamics (FMetaD), where a funnel shaped restraint potential is applied to the system to reduce the space explored by the ligand once it is unbound, thus reducing computational cost even more . In FMetaD, the bias potential is typically applied to the distance between the ligand and the binding site, thus, sampling multiple binding/unbinding events within simulation. FMetaD allows identification of the ligand binding mode, absolute protein-ligand binding free energy, insight into the dynamics of the ligand binding, including the presence of alternative ligand binding modes and other energetically relevant states (e.g., transition states). Furthermore, it has been demonstrated that FMetaD can be used to study ligand binding process in challenging systems where binding site is buried or protein undergoes significant conformational changes upon binding . FMetaD has demonstrated to be able to predict ligand binding free energy with <1.5 kcal/mol accuracy . It is important to note, however, that FMetaD is an MDbased protocol and the correctness of the results depend on the accuracy of the protein and ligand force-field parameters, as well as system amino acid and ligand protonation state selection. By examining known non-peptidomimetic plm inhibitors, we previously hypothesized , that one of the key features enabling the ligand binding to the enzyme in open flap conformation might be hydrophobic substituent capable of aromatic interactions with Trp41, Tyr77 and/or Phe111 (plm II numbering) under the flap loop. To test this hypothesis, we designed and synthesized a series of 2-aminoquinazolin-4(3H)-one based inhibitors (see Figure ) with hydrophobic substituents bearing benzene group at various locations, determined their activity against plm II, IV, V and cat D, and explored their binding pathway using funnel metadynamics simulations. This showed that the inhibitor binding mode can be estimated using metadynamics simulations, and information obtained can be used in selective inhibitor design.