BEST PRACTICES

Schrödinger Solutions for Small
Molecule Protonation State
Enumeration and pKa Prediction
Executive Summary
The pKa of a drug is a key physicochemical property to consider in the drug discovery process
given its importance in determining the ionization state of a molecule at physiological pH.
Schrödinger provides several solutions for predicting pKa values, protonation state distribution
and derived properties that can be applied across a range of drug discovery stages, fromscreening through lead optimization. Here we provide an overview of each technology solution
and use case examples of how they can be applied in drug discovery.

Background
Small molecules can undergo ionization in solution where they either lose or gain protons (H+) at
different ionizing sites. The measure of the propensity of a site or molecule to ionize by theassociation/dissociation of one or more protons is quantified by a pKa value. If the pKa valuerefers to a particular site ionizable site the value is a microscopic pKa (micro-pKa), and it is amacroscopic pKa (macro-pKa) if the value refers to the entire molecule. The specificarrangement of protons around the ionizing sites constitutes a protonation state, and different
protonation states of the same charge level are called tautomers. Each protonation state is inthermodynamic equilibrium with the others and therefore has a free energy associated with its
population within this collection of protonation states, which may be derived either frommicro-pKa values through thermodynamic equations or obtained directly by comparing the freeenergies of the states. In drug design, understanding the different protonation states of amolecule is critical, since they will drive properties including solubility, membrane permeability,and activity.

1Challenges of pKa Prediction
Determining which states predominate at a given pH and by how much is a challenging task
both experimentally and computationally because the number of states that are all inthermodynamic equilibrium grows ~2n with the number, n, of singly protonatable sites. Thus,
molecules with many titratable sites can potentially have a large number of different protonation
states, all of which need to be enumerated and energetically scored.
Computationally, Schrödinger uses two main approaches to score states: 1) through evaluating
thermodynamic equilibrium equations with micro-pKa values, and 2) directly predicting thestates’ relative free energies. Predicting pKa values is an important step to calculating state
distributions, which in turn enables prediction of important related quantities that would
otherwise be inaccessible.

Figure 1. Relationships between macro-pKa,
micro-pKa, protonation states, and tautomers andthe corresponding speciation diagram.

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Overview of Schrödinger Solutions
Epik Classic

Jaguar pKa

Epik Classic, previously known simply as Epik1,
is an expert system for rapidly and accuratelypredicting the micro-pKa values and the mostpopulated protonation states for a ligand at agiven pH. The underlying pKa prediction
technology is the empirical Hammett-Taft linear
free energy relationship (LFER), which identifies
an ionizing group, takes its root pKa value,
perturbs it by the bonded chemical fragments,
and applies charge spreading to arrive at its
effective micro-pKa value. Epik Classic then uses
the predicted pKa values to enumerate a ligand’s
protonation states, rank them by energy, andthen return the most populated states. Because
Epik Classic uses SMARTS patterns-based
rules, it is fast enough for high-throughput,
although at the expense of being unaware ofboth conformational and stereochemical effects.

Jaguar pKa takes a third, more physics-based
approach to predicting micro-pKa values for aligand. This workflow calculates the pKa values
at the user-defined ionizing sites in a query
ligand by first generating the conjugate pair, on
which are then executed conformational
searches to locate the lowest energy structures,3
followed by density functional theory (DFT)
based geometry optimizations and single-point
energy evaluations. These resulting
conformationally-averaged, “raw” micro-pKa
values are then corrected using
empirically-parametrized relationships to giveaccurate predictions. Jaguar pKa performs best
on non-tautomerizable structures. Being
physics-based, it does take into accountgeometric and stereochemical effects, but at theexpense of speed.

Epik 7

Macro-pKa

Epik 72 is a complete redesign of Epik thatleverages Schrödinger’s powerful machinelearning (ML) technology for more accurate
results across broader chemical space. Ionizing
groups are initially identified by SMARTS
patterns and are then used to enumerate theprotonation states for a range of ionizations. The
micro-pKa values of each site in each state are
predicted with 3-layer atomic graphconvolutional neural networks (GCNNs)
extending out radially six bonds from the ionizing
atom. The predicted pKa values for the states
are then used to predict the relative energies ofthe states to both allow determination of themost populated states at a pH and calculation ofmacro-pKa values. The topological nature of theML approach means that Epik 7, like previous
versions, is rapid but agnostic to 3D geometry
and stereochemistry.

Macro-pKa follows the same philosophy as
Jaguar pKa by combining physics-based DFT
calculations with empirical corrections, but
extends its applicability to enable calculation of
tautomerizable ligands. Macro-pKa automatically
identifies ionizing sites, enumerates the
protonation states, and calculates the micro-pKa
values following a similar workflow to JaguarpKa, but with an enhanced scheme forgenerating empirical corrections. Finally, thecalculated micro-pKa values are used to rank theprotonation states by energy, return the mostpopulated states for a user-supplied pH, anddetermine the macro-pKa values for the ligand.
The exhaustiveness of this approach comes at alarger time and resource cost than Jaguar pKa.

3Use Cases
Here we outline several use cases for pKa prediction in the drug discovery workflow.
Note: Each use case example outlines below could be approached with any of the listedsolutions within that section. The dataset presented highlights the applicability of just one of thepossible solutions.

I. Querying microscopic pKa values
Applicable Solutions●●●Epik Classic
Epik 7Jaguar pKa

When investigating the binding modes of a ligand, the micro-pKa value of an ionizing site is anindicator of the propensity for it to become ionized at a given pH. The ionization state of theligand directly influences how it interacts with another molecule such as a protein, e.g., whether
or not it can participate in a salt bridge.

Figure 2. Jaguar pKa micro-pKa predictions for a dataset of small molecules.

4II. Querying apparent or macroscopic pKa values
Applicable Solutions●●Epik 7
Macro-pKa

For monoprotic or polyprotic compounds with a single dominant tautomer at each charge level,
micro-pKas may very closely match the apparent or macro-pKa value that is most commonly
obtained through titration experiments. However, for compounds or ionization states withmultiple competitive tautomers, the micro-pKa value of a single tautomer may not fully reproducethe experimentally observed macroscopic value. To obtain this apparent value, all states’ mustfirst be enumerated and evaluated so that all their micro-pKa values are considered in themacro-pKa calculation.

Figure 3. Macro-pKa macro-pKa predictions for a dataset of tautomeric molecules.

5III. Ligand preparation and high-throughput screening
Applicable Solutions●●Epik Classic
Epik 7

Physics-based simulations typically require specification of all atoms in the simulation system,
including all hydrogen atoms. Thus, structure-based simulations including Glide docking,molecular dynamics, and free energy perturbation with FEP+ should be performed using anensemble of the highly-populated protonation states of a ligand. Therefore, a crucial first step inany structure-based screen of a small molecule ligand library is to prepare the ligands byobtaining the most populated protonated states. Epik Classic and Epik 7 are integrated with ourautomated ligand preparation workflow, LigPrep, to allow preparation of large ligand libraries forhigh-throughput screening. Additionally, both Epik Classic and Epik 7 and their LigPrep
implementations allow for the generation and scoring of additional states that may potentiallybind to metal ions in the pocket.

Figure 4. Epik Classic micro-pKa predictions for a dataset of 152 drug molecules

6IV. Hit-to-lead optimization
Applicable Solutions●●Epik Classic
Epik 7

Once hits are identified, a series of analogs are synthesized to explore the relevant chemical
space in greater detail to arrive at improved behavior. It is important to be able to screen
potential candidates rapidly and accurately to assess which to optimize further. The < 0.5 log
unit accuracy and sub-second calculation speed of Epik Classic and Epik 7 make themexcellent tools for rapid idea generation and testing. In addition to pKa value and protonation
state distribution prediction, they have been implemented in other ADMET or property
predictors, such as for membrane permeability and solvation energy.

Figure 5. Epik 7 macro-pKa predictions for a dataset of congeneric tricyclic thrombin inhibitors.

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V. Early-stage lead optimization
Applicable Solutions●●●Epik 7Jaguar pKa
Macro-pKa

Optimizing the many physical characteristics required can be laborious and costly, from ideation,
through synthesis and assay. In this environment, where high quality property predictions arerequired and time permits, Schrödinger’s physics-based predictors, Jaguar pKa and Macro-pKa,take into account more molecular characteristics, including conformational and stereochemical
effects to improve pKa prediction accuracy.
Additionally, Macro-pKa and Epik 7 both offer detailed speciation reports for a queried ligand.
These are especially helpful for understanding the distribution of tautomeric states across thepH spectrum.

Figure 6. A Macro-pKa report detailing the macro-pKa value and the distribution of protonation
states across a pH range.

8Feature Comparison Table
Feature\Program

Epik Classic

Epik 7Jaguar pKa

Macro-pKa

Technology

Hammett-Taft
LFERs

AtomicGCNNs

DFT +EmpiricalFitting

DFT +Empirical Fitting

2007

2022

Year Introduced

2005

2023

aIonization Enumeration Distance±5±2±1±2a

Average Single Ligand Run Time

0.2 s

0.4 s

1h1d

Practical Maximum Ligand Size

150 atoms

200 atoms

100 atomsb

100 atomsb

Predicts micro-pKa values

Yes

Yes

YesYesPredicts macro-pKa values

NoYes

No

Yes

Enumerates States

Yes

Yes

No

Yes

Predicts Populations

Yes

Yes

NoYes

Accurate pKa values

Yes

Yes

Yes

Yes

Remote IntramolecularInteractions

NoNo

Yes

Yes

Conformational Effects

No

No

Yes

Yes

Stereochemistry

NoNo

Yes

Yes

Pseudochirality

No

No

No

Yes

DMSO Solvent

Yes

No

Yes

No

Metal Binding States

Yes

Yes

No

No

Trainable

No

Yesc

Yes

Yesc

Panel

Yes

Yes

Yes

Yes

Speciation Report

NoYes

No

Yes

LigPrep Integration

Yes

Yes

No

No

aEasily adjustable; b Strongly influenced by the number of conformers (and tautomers in
Macro-pKa); c Only by internal experts at this time.
Table 1. Comparison of Features of the Small Molecule Protonation State Enumeration and pKa
Prediction Technologies

9References(1) Shelley, J. C.; Cholleti, A.; Frye, L. L.; Greenwood, J. R.; Timlin, M. R.; Uchimaya, M. Epik:
A Software Program for pKa Prediction and Protonation State Generation for Drug-like
Molecules. J. Comput. Aided Mol. Des. 2007, 21 (12), 681–691.
(2) Johnston, R. C.; Yao, K.; Kaplan, Z.; Chelliah, M.; Leswing, K.; Seekins, S.; Watts, S.;
Calkins, D.; Elk, J. C.; Jerome, S. V.; Repasky, M. P.; Shelley, J. C. Epik: pKa andProtonation State Prediction through Machine Learning. J. Chem. Theory Comput. 2023, 19(8), 2380–2388.
(3) Bochevarov, A. D.; Watson, M. A.; Greenwood, J. R.; Philipp, D. M. Multiconformation,Density Functional Theory-Based pKa Prediction in Application to Large, Flexible Organic
Molecules with Diverse Functional Groups. J. Chem. Theory Comput. 2016, 12 (12),6001–6019.

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