How to Reward Your Drug Agent?
Abstract
Constructing novel molecules from scratch using deep generative models provides a valuable alternative to traditional virtual screening methods, which are limited to searching the already discovered chemicals.
In particular, molecular optimisation combined with sampling guided by reinforcement learning seems like a promising path for discovering novel molecular designs and allows for domain-speciﬁc customisation of the desired solutions.
The choice of a chemically relevant reward function and the exhaustive assessment of its properties remains a challenging task.
We introduce the reward function which gives enough ﬂexibility to quantify the biological activity with respect to a selected protein target, drug-likeness, synthesizability and incorporates the custom index of penalised physico-chemical properties.
In order to customise the hyper-parameters inﬂuencing the RL agent performance, we propose the methodology that helps quantify the chemical relevance of the reward function by quantifying the chemical signiﬁcance of the samples.
We assess the performance of the reward function by docking the molecules with relevant protein targets and quantify the difference with the ground truth samples using
Wasserstein distance.
Introduction
The chemical space of accessible and stable small molecules from which potential candidates for drug design can be selected consists of about 10 60 compounds [Bohacek et al., 1996].
As the search is computationally intractable [Sanchez-Lengeling B, 2018], it is critical for the molecular discovery to generate a pool of candidate molecules that satisfy problem-speciﬁc properties.
For early detection of bad leads, scores based on physico-chemical properties [Lipinski et al., 1997, Bickerton et al., 2012] and comparisons to already discovered topological structures provide a useful starting point and benchmark.
Nevertheless, these approaches introduce bias and inefﬁciencies into the inevitable automation of the drug discovery process
Molecular optimisation uses computational methods to construct compounds that can be sufﬁciently small and show bio-activity with protein targets of interest [Sanchez-Lengeling B, 2018].
The drug activity is deduced by its afﬁnity to a target.
Structure-based screening utilises in-silico modelling approaches that encompass structural information of the drug target.
A classic modelling technique in this category is molecular docking the biologically active molecule (ligand) is scored based on the ligand conformation at the protein binding pocket.
Optimal placement of the ligand is generated by optimising the conformation of the ligand to minimize the scoring function [Halperin et al., 2002].
Compared to other early-stage virtual screening methods, molecular docking is computationally expensive [McGaughey et al., 2007], which indicates that for a truly high-throughput virtual screening system, candidate molecules need to be smartly pre-selected so that only a tiny fraction of the potential chemicals is being assessed.
Directed generative models, such as VAEs [Kingma and Welling, 2014, Rezende et al., 2014] have been successfully used in inverse designs [Sanchez-Lengeling B, 2018] to generate novel chemical structures [Gómez-Bombarelli et al., 2018, 2016].
VAEs typically learn to sample the structured molecular data in the form of a SMILE string [Weininger, 1988], which describes the schematic graph topology of the molecule using the underlying grammar.
The original pitfalls associated with this approach such as generating semantically correct SMILE string which lacks the chemical meaning has been addressed by various researches leading to improvements of reconstruction quality of the generated SMILE strings by learning the underlying grammar of the strings [Kusner et al., 2017, Jin et al., 2018].
Because VAE compresses the information from the data with the help of
KL-divergence regulariser, we can expect that the learnt latent representations captures the similarities based on the underlying grammar rules used for the construction of the SMILE strings rather than on physico-chemical properties of the molecules [Karlova et al., 2021].
For molecular optimisation, it is desirable that the molecules are embedded into the latent space which is smoothly organised according to the chemical properties and consequently allows for numerical optimisation [Grifﬁths and Hernández-Lobato, 2020, Zhavoronkov et al., 2019].
Also using the hierarchical prior allows for improved reconstruction and has sufﬁcient capacity to better capture the relationships between latent representations.
The sampling from the latent space can be guided by reinforcement learning [Popova M, 2018, You et al., 2018, Zhavoronkov et al., 2019].
The generation of the new molecule is decomposed as a sequence of actions while biasing the generation towards the compounds that optimise the selected reward function.
The choice of reward function proves to be equally challenging.
Ideally, the task requires ﬁnding the reward function which in a comprehensive way quantiﬁes the bonding probability with the protein target, drug-likeness and synthesize-ability.
Popova M [2018] considered quantitative structure-activity relationships models (QSAR)[Hansch et al., 2001] rewards, while [Zhavoronkov et al., 2019] suggests using a combination of self-organising maps (SOMs)
[Kohonen, 1990] ﬁtted to target afﬁnities.
The benchmarking platforms [Polykovskiy et al., 2020, Grant LL, 2021, Brown, 2019] were introduced to compare the drug generative pipelines in a standardised manner.
These platforms provide metrics which help to detect over-ﬁtting, imbalance of frequent structures or mode collapse, among others.
In particular, the metrics quantify the validity and uniqueness of the generated SMILEs, novelty, fragment and scaffold similarity, similarity to the nearest neighbours, internal diversity and
Frechet ChemNet distance [Brown, 2019].
Nevertheless, these measures are insufﬁcient to assess the chemical relevance of the reward function.
In this paper we address the problem of how to realistically choose the reward function in the molecular optimisation guided by reinforcement learning.
We propose the methodology which helps to quantify the chemical relevance of the reward function.
At the core of our method is the quantiﬁcation of the distances between probability distributions of the biologically active candidates and the candidate samples.
In particular, we investigate the joint probability distributions of the docking scores and other physico-chemical properties of the generated samples from the RL agent.
We compare these to the same probability distributions of the benchmark bio-active molecules and the decoys.
We use optimal transport metrics to quantify this distance.
We also propose a tuneable reward function which is a linear combination of the predicted bio-activity and higher level metrics of druglikeness and chemical and synthetic feasibility.
We examine its performance with our methodology to demonstrate its usefulness.
The paper is organised as follows: in the next section we discuss factors important for the selection of preferable drug-like molecules such that it is possible to capture typical benchmarks and medicinal chemistry requirements.
In section 3 we introduce the DAFT reward function and discuss how to tune its hyper-parameters.
In the section Experiments we give an example of the DAFT reward setting and evaluate its performance on the DUD-E dataset [Mysinger et al., 2012] for GRIA2, DRD3, ESR1 and ABL1 protein receptors.
Drug-likeliness Feasibility Criteria
For a medical chemist, a set of de novo molecules produced by various ML generative models is a shamefully often low starting point as the predicted compounds may contain problematic reactive groups, the structures are hard to synthesize or possess suboptimal physiochemical properties [Renz, 2019].
Therefore, the post hoc assessment by experienced wet lab personnel is required, with their time often being wasted on ineffective, unstable, not synthesizable and otherwise problematic non-druglike compounds.
In a drug design project, a speciﬁc macromolecular target such as a particular receptor or certain enzyme is targeted with the disease process implying this macromolecular target. The goal is to ﬁnd safe, tolerable small molecules that modulate the functioning of the macromolecular target and modulate the role it plays in the disease process. In order to assess how strongly a small molecule modulates its intended target, we measure the afﬁnity.
Afﬁnity is measured and expressed in units such as Ki, Kd, IC50, depending on the measuring assay used and the type of activity.
Predicting the afﬁnity of a molecule at its target is a challenging undertaking. Even very advanced modelling techniques do not capture subtle effects like long term dynamics and solvent effects. Nonetheless, by ﬁnding patterns and recurring structural features in molecules with known afﬁnity, it is possible to estimate potential activity and ﬁlter compounds in a way that enriches the active compounds of the remaining molecular set.
The druglikeness of a compound is determined by a wide balance of factors, including chemical stability, lack of problematic substructures (such as PAINS or reactive functional groups), advantageous physiochemical properties that will affect pharmacokinetics and a good spatial distribution of functional groups corresponding to good ligand efﬁciency and binding site complementarity.
Speciﬁc rules of thumb exist to assess druglikeness, most famously Lipinski’s rule of ﬁve, which sets maximal ranges for hydrogen bond acceptors, hydrogen bond donors, lipophilicity and molecular weight. The
QED-score, quantitative estimate of druglikeness [Bickerton et al., 2012] is a single weighted score of 8 calculated properties of molecules, concretely molecular weight, partition coefﬁcient, hydrogen bond donor count, hydrogen bond acceptor count, polar surface area, rotatable bonds, number of aromatic rings and the presence of any structural alerts.
These rules are not strict: there are classes of drugs that strongly violate these rules and yet are safe marketed drugs, such as the group of macrolide antibiotics that includes WHO essential medicine Erythromycin.
Conversely, there are also compounds which have near optimal QED-scores, because highly unstable and exotic molecules can also meet all the formal criteria, see Figure 1. These more crude druglikeness metrics are often not enough.
Synthesizability, in the case of de novo molecular generation, and commercial availability, in the case of an extensive pre-existing screening library, are also important for practical aspects.
Compounds need to be procured with minimal effort to use available resources efﬁciently.
A standard metric for synthesizability is the SAScore [Ertl and Schuffenhauer, 2009], which determines synthetic accessibility in a rule based way and correlates quite well with real life synthesizability, addressing some of the issues with non-druglike molecules that have good QED scores.
The SAScore distribution of commercially available compounds, druglike compounds and natural product-like compounds is also signiﬁcantly different.
The amount of sp 3 hybridized carbons has been used as a crude proxy for 3-dimensionality of compounds, although this does not take the complexity of the framework into account, whereas MCE-18 [Ivanenkov et al., 2019] will assign a score for non-trivial tetrahedral carbons that are not just long linear hydrocarbon segments.
More subtle qualities like sphericity, non-ﬂatness and ﬂexibility have also been suggested as important.
In fact, when a molecule is very ﬂexible (estimated by the number of rotatable bonds) structure-based drug design methods based on pose sampling, such as pharmacophore modelling and molecular docking, will encounter problems. When a set of molecules is considered the coverage of chemical space, i.e. the diversity of the set is also important, [Feher and Schmidt, 2002, Ajay et al., 1998].
Methods
We aim to quantify the performance of the distribution learning models.
Consider a sample D from the oracle p data ( x ) generating the molecules.
The generative model q ( x ) approximates the true generative process.
We consider a ground truth sample of biologically active molecules and decoys available in the chemical databases such as CHEMLb [Mendez et al., 2018].
The sampling of the SMILE string has been formulated as a reinforcement learning problem.
The state space S is the collection of partially or fully constructed SMILE strings, state s t corresponds to the string constructed up till character t .
The action space A consists of the dictionary of the characters used by the SMILE grammar and so action a t is a character which will be appended to the string s t .
The transition function T : S ×
A −→ S is learnt by VAE.
Next we denote the reward function as R : S ×
A −→ R .
Each episode corresponds to appending new character to the existing SMILE string, so the maximal number of episodes T is the maximal length of the SMILE string we want to generate.
The environment is the SMILE string generating process modelled by the decision Markov process M = ( S , A , T , µ 0 , γ, R, T ) , where µ 0 is the initial state distribution and γ the discount factor.
Construction of the new molecule is a task for stochastic optimisation, where we search for the policy π θ that maximizes the state-action trajectories in the environment, i.e.
V π ( s ∗ t ) =
E π (cid:80) Tt
= t ∗ γ t R ( s t , a t
[Equation]
s ∗
[Equation]
The choice of the reward function determines the overall value and selected trajectories in actionstate space.
We propose to use the reward function which gives enough ﬂexibility to quantify the biological activity with respect to selected protein target A ( s t , a t ; ρ ) , drug-likeness QED ( s t , a t ) , synthesizability SA ( s t , a t ) and mainly the incorporate the custom index of penalised physicochemical properties CP ( s t , a t ) .
We deﬁne the drug-likeliness and predicted ligand afﬁnity to a protein target (DAFT) reward function as:
[Equation]
(1) where m ≡ m ( s t , a t ) denotes the molecule corresponding to SMILE s t with concatenated a t , ρ is the protein target and τ is a penalties vector corresponding to vector of ﬁlters from the space of hard constrains F .
For example if the molecule m ( s t , a t ) constructed from the SMILE ( s t , a t ) is not chemically valid, we set the penalty corresponding to the chemical non-validity.
Alternatively, if the molecule is chemically valid but not biologically active, we can penalise for this too.
The hyper-parameters κ i , i = 1 . .
. , 4 allow for customising the importance of particular component of the reward function and also for absorbing the different scales of the used factors.
Term A ( s t , a t ; ρ ) corresponds to the functional of the afﬁnity and quantiﬁes the experimental protocol of the binding to a target ρ , e.g. PKi, EC50, docking score or models predicting these quantities, such as SOMs or
QSARs type predictors.
The custom property allows for hand-crafting rules which can incorporate various functional relationship of underlying physico-chemical variables, e.g. charge, docking score, weighted average of measures describing the binding ﬂexibility of the molecule, measures of molecular similarity or any other desirable feature.
The selection of the hyper-parameters κ i , ﬁlters and construction of custom property allows for targeting various illnesses, e.g. targeting distribution to a speciﬁc tissue type or organ to modulate a cancer involved kinase tissue-speciﬁcally.
The high tunability of hyper-parameters κ i allows for sufﬁcient ﬂexibility but requires monitoring of the quality of the sample.
The standard measures which monitor the sample diversity, novelty or
FrechetChemNet distance are insufﬁcient for this task.
We use the following method: consider the sample of the ground truth data, which has relevant labels describing bio-activity, docking score, chemical properties and other scoring metrics.
Split the control sample into categories based on the desirable property, e.g. actives and decoys.
Compute docking scores of the generated sample and the protein target.
Molecular docking is a technique used to estimate non-covalent complexes, usually either between two proteins or between a protein and a small molecule.
In the context of a ligand-receptor complex, docking has two main intended outcomes, predicting the pose of the ligand in the receptor pocket and estimating the binding activity.
A energy-like scoring function is used to assess a certain pose of the ligand, the docking algorithm will try optimize the scoring function and return the best-scoring pose.
Consider joint probability distribution q ( d i , c i ; m, ρ ) of the docking scores
d i between the molecule m and protein target ρ and a vector of chemical properties c i of the molecule m .
We would like to compare the distance between our molecules sampled by the RL agent and ground truth distribution p data ( ¯ d i , ¯ c i ; ¯ m, ρ ) .
The samples from distribution q can have a low variance due to constrains in the reward function and therefore be concentrated on the small regions, whereas the true distribution p data can spread above manifolds of various shapes.
Choosing KL-divergence would lead to mis-leading results because KL-divergence goes to inﬁnity when the distributions approaches Dirac measure.
To overcome this obstacle we use the Wasserstein distance to quantify the aforementioned distance.
In particular we compute the optimal transport between the empirical distributions using the Sinkhor algorithm.
[Chizat et al., 2020].
Experiments
To validate our proposed methodology, we use two benchmark datasets: DUD-E, the Directory of Useful Decoys [Mysinger et al., 2012], and MOSES, the Molecular Sets [Polykovskiy et al., 2020].
DUD-E dataset is a collection of 102 protein targets and related 22886 ligands from CHEMBl [Mendez et al., 2018], manually selected database of bioactive molecules with drug-like properties.
In DUD-E, each ligand is complemented with 50 decoys from ZINC database [Irwin et al., 2012] and has chemical properties similar to the particular ligand.
Otherwise the decoys are selected to be topologically dissimilar to the ligands and only the most dissimilar decoys are included.
The ligands for each target are selected such that the chemotype diversity is also guaranteed.
MOSES is a benchmarking platform introduced to help to standartise the training and comparisons of generative models of the molecular designs.
The dataset available with the platform contains 1.9 million compounds hand-selected from ZINC such that the internal diversity is guaranteed.
We use the train and test splits as provided with training set of 1.5 million compounds.
We examine the modiﬁcation of the architecture inspired by Zhavoronkov et al.
To learn the distribution of the SMILE string from MOSES training set, we use supervised VAE with 16-dimensional latents, hierarchical prior represented as Gaussian-mixture and RNN based encoder and decoder architecture.
See Appendix A for details on the architecture and training.
From 102 DUD-E targets we select 4 protein targets ρ : Dopamine D3 receptor (DRD3), Estrogen receptor alpha (ESR1), AMPA 2 ionotropic glutamate receptor (GRIA2) and Tyrosine-protein kinase
ABL (ABL1).
These targets were selected because they have a large amount of experimental biological data deposited and because they correspond to 4 typical protein families targeted in drug development campaigns: a membrane receptor, a nuclear receptor, an ion channel and a kinase enzyme, respectively.
Activity at the four targets of interest was estimated using a random forest classiﬁer and a random forest regressor trained on bio-activity data and ECFP6 ﬁngerprints generated from the structures of active compounds and decoys deposited in DUD-E. For typical QSAR tasks, including prediction of activity at DUD-E targets, random forests using folded circular ﬁngerprints are a standard technique and generally tends to have a performance
We explored the performance of the agent with hand-picked DAFT reward of the following form: [Equation] if [Equation]
is a valid ligand
[Equation]
if [Equation] decoy [Equation]
chemically
[Equation]
The goal of this function is to encourage valid, MCF and PAINS Filter passing molecules, that are also drug-like and synthesizable, resemble CNS penetrating compounds and present non-decoy like interaction with speciﬁc targets (pKi).
We consider two penalties ﬁltering for chemically valid molecules and biologically active ones.
The details on the CP constructions are in Appendix
A.2.
To explain the motivation behind the choices of parameters κ i = 1 , . .
, 4 corresponding to vector (0 . 1 , 1 ., 0 . 2 , − 0 . 1) : value SA(m) is in range between 1 to 10 with value 1 indicating the best synthesize-ability, therefore the choice of − 0 .
We search for pKi values which are between 6 and 10, therefor we scale down the term A ( m, ρ ) with choice 0 .
Next we train the RL-agent for 8000 epochs using REINFORCE algorithm.
The explorationexploitation rate is set to 30% and 70% of 200 samples.
We use Adam optimizer with learning rate 2e-5.
The linear layer applied to the latent space samples in the decoder is also trained with an Adam optimizer with a learning rate of 1e-6.
Labeled Samples Pairs
A D
A As
A Ds
D
As
D Ds
As Ds
MolWt 0.
TPSA 0.
MolLogP 0.
FractionCSP 0.
NHOHCount 0.
NOCount 0.
NumHAcceptors 0.
NumHDonors 0.
NumRotatableBonds 0.
RingCount 0.
NumAromaticHeterocycles 0.
NumAliphaticHeterocycles 0.
NumAromaticCarbocycles 0.
NumAliphaticCarbocycles 0.
[Table]
label
Samples produced from the RL agent trained with reward DAFT(m; DRD3), DAFT(m; ESR1),
DAFT(m; GRIA1) and DAFT(m; ABL1) were docked with the corresponding protein targets.
For details on the docking see Appendix A.3.
Ground truth samples p data ( ¯ d i , ¯ c i ; ¯ m, ρ ) for benchmarking the DAFT agent were constructed from DUD-E ligands and decoys. We also computed basic physicochemical molecular descriptors and we constructed the joint distribution with the docking scores. These descriptors include: molecular weight, partition coefﬁcient of the molecule (log P), fraction of sp 3 -hybridized carbon atoms and number of rotatable bonds, a measure of ﬂexibility, count of NH and OH, total ring count, and speciﬁcally number of aromatic heterocycles, number of aromatic carbocycles, number of aliphatic heterocycles, number of aliphatic carbocycles, total polar surface area and quantitative estimation of drug-likeness (QED-score)
[Bickerton et al., 2012].
We depict the dependencies of the chemical properties and docking score of each sample in the Figure 2.
Next we take DUD-E actives and DUD-E decoys.
We also take the sample generated by RL agent with DAFT(m; DRD3), DAFT(m; ESR1), DAFT(m; GRIA1) and DAFT(m; ABL1) and label them with QSAR-RF binary classiﬁer with active or decoy label.
Then we compute the optimal transport matrix using Sinkhor algorithm
[Flamary et al., 2021] with Square Euclidian norm as a cost function.
We use this for computing the Wasserstein norm between the joint distributions of the docking scores and particular chemical property (as depicted in Figure 2) of different groups of molecules.
In particular we consider pair of ground truth actives and ground truth decoys (A D), ground truth actives and sampled actives (A As), ground truth actives and sampled decoys (A Ds), ground truth decoys and sampled actives (D As), ground truth decoys and sampled decoys (D Ds), sampled actives and sampled decoys (As Ds).
See Table 1 for the results.
The values in column ’As Ds’ of Table 1 indicates that the distributions of the generated decoys and actives are very close to each other and does not reﬂect the behaviour of the distances between ’A D’ pair.
Visual inspection of the distributions in Figure 2 indicates that κ = 1 .
in QED ( m ) term of the DAFT reward function pushed the optimisation into region of high QED values.
Further CP term contains constrain on the number
[Table]
[Table]
[Table]
[Table]
CP [Table] [Table] of carbon rings which further restricts the region for the potential candidates
We can conclude that the parameter setting in our experiment leads to over-optimisation which restricts the selection of the potential candidates.
Ideally, we would like to optimise our hyperparameters κ , such that they better reﬂect the region of the biologically active candidates.
In the second experiment we focus on ESR1 receptor and vary the hyper-parameters κ i , i = 1 , . . .in
the reward function.
We consider two sets of experiments where we multiply only one of the κ i s by 0 . 5 , 2 , 10 while keeping other κ s unchanged. This allows us to investigate the impact of each term in the reward function separately. We also study cases where we multiply all terms of the reward function by values 20 , 50 , 100 .
We observe that the values of the Wasserstein distances changes conditioned on the particular chemical property.
This experiment indicates that there is a risk of the inherent bias while hand-picking the hyper-parameters due to the human expert demand which can be in contradiction of the inner bias of the optimisation procedures in the ML model.
The automated hyper-parameter optimisation can potentially help to tune the human expert requirements and the model design induced biases.
See Table 2 for the results for total polar surface area.
We provided a practical methodology which we believe other researchers interested in generative models for molecular drug designs ﬁnd useful when constructing the experiments evaluating the performance of newly developed modelling approaches.
In particular, we stated requirements on the reward function usable for the generative models of the novel chemical structures.
Our proposal is indeed incomplete and suffers from shortcomings such as dependence on other predictors which potentially accumulates the model error [Sculley et al., 2015], so monitoring the uncertainty of the predictors for biological activity used in the reward functions is inevitable component of the model-pipeline.
We discussed the biases introduced by the scores designed to quantify physico-chemical properties.
Biases of this type will be shared with other compound scoring functions, either human expert designed or machine learnt.
The proposed function is general enough to target a wide spectrum of biological targets, but it is important to note that negative aspects, such as toxicity, environmental accumulation, side-effects, are not robustly protected against.
Therefore, care must be taken to not rely blindly on compounds privileged by this workﬂow.
A Experiment Details A.1 VAE
We use one-hot encoding for the SMILEs string as in Gómez-Bombarelli et al.
We examine the modiﬁcation of the architecture inspired by Zhavoronkov et al.
To learn the distribution of the SMILE string, we use supervised VAE with 16-dimensional latents.
The VAE encoder architecture is: a 2-layer bidirectional GRU with hidden dimension of 256 followed by 2-layer MLP with dimensions: 256, 32 and Leaky
Relu activation with slope 0.1.
The decoder architecture is: 1-linear layer: 128, dropout rate 0.2, followed by 3- layer GRU with 128 hidden dim followed by scaling linear layer matching the length of the input SMILE, which we set to 47.
We train the decoder in teach-forcing manner, i.e. both the SMILE and its latent representation are pass to GRU-layers, while maximizing the logits corresponding to the correct tokens in the initial SMILE.
We use structured prior distribution π ( z ) s which is composed of mixture of Gaussians with mixture weights learned by tensor-train decomposition parameter as in [Oseledets, 2011, Zhavoronkov et al., 2019].
The chemical properties used in supervised training are: SA, QED and CP, see Figure 3 for visualisation of the trained latent space.
[Table]
SA score and QED corresponds to 1-Wasserstein distance computed between the sample from RL agent and reference sample from test set
VAE is trained for 10 epochs on MOSES training dataset using Adam optimizer with a learning rate of 1e-3 with batch-size 256.
A.2 Custom Property Setting
The custom property CP is designed to yield values close to 5 for molecules that resemble CNS penetrating drugs, similar to CNS_MPO, as deﬁned by value ranges for the following properties: log P , number of hydrogen bonds donors, number of hydrogen bonds acceptors, number of aromatic rings and the topological polar surface.
We split the values of the properties into custom ranges and assign them a custom weights as follows: for log P we have values -2, 0, 4, 5 and 7 with associated weight vectors for the corresponding intervals (0.05, 0.05 to 1., 1., 1. to 0.3. , 0.3 to 0.05, 0.05). For the number of H-bond acceptors we have values 2 and 6, with weights (1., 1. to 0.05, 0.05). For the number of aromatic rings we have values 0, 1, 2 and 5 with weights (0.8, 0.8 to 1., 1., 1.
to 0.4, 0.4).
For the topological polar surface area we have values 60, 100 and 140 with weights (1., 1. to 0.9, 0.9 to 0.59, 0.59). For the number of H-bond donors we have values 1, 3 and 4 with weights (1., 1. to 0.9, 0.9 to 0.6, 0.6).
A.3 Docking
Molecular docking was performed using AutoDock Vina 1.2.0, an open-source tool for molecular docking [Eberhardt et al., 2021].
Protein and ligand preparation were standard: protein structures were extracted from the RCSB protein database and split into receptor and ligand.
The receptor was prepared for docking using the ADFR Suite.
All ligands were preprocessed using the Meeko package.
Initial 3D conformation were generated using the MMFF forceﬁeld.
The coordinates of the ligand were extracted and used as the coordinates for the grid box.
[Table] [Table] [Table] [Table]
[Table]
[Table] [Table]