Accelera'ng Drug Discovery with Free Energy Calcula'ons on GPUs - PowerPoint PPT Presentation

Accelera'ng Drug Discovery with Free Energy Calcula'ons on GPUs Robert Abel VP, Scien1fic Development

Drug Discovery • “It’s damned tough to discover a drug.” –Eugene Cordes • The mission of Schrödinger R&D is to make this much easier

Lead Op'miza'on is Profoundly Challenging Total Costs Phase of Drug Discovery (USD millions) Target-to-hit $94 Hit-to-lead $166 Lead-op1miza1on $414 Preclinical $150 Phase I $273 Phase II $319 Phase III $314 Submission to launch $48 Total $1,778 Paul SM et al . Nat. Rev. Drug Disc. 9:203-214. 2010.

Free Energy Calcula'ons Should be able to Help • Faster potency op1miza1on with fewer synthesized compounds • Be]er maintenance of potency while tuning ADMET proper1es • Account for other proper1es relevant to lead op1miza1on – Binding selec1vity – Muta1onal resistance – Solubility – Membrane permeability

Rela've Binding Free Energy Calcula'ons with FEP • Compu1ng rela1ve free energies has notable advantages A – Modeling of smaller perturba1ons should be more accurate – Rela1ve differences are o`en of 1 2 greatest interest in lead op1miza1on B • Instead of modeling the full binding process, we use FEP to compute – the difference between ligand 1→2 in ΔΔ G binding = Δ G 1 – Δ G 2 solu1on ( A ) ΔΔ – the difference between ligand 1→2 in = Δ G A – Δ G B the binding site ( B )

Schrödinger’s Approach: FEP+ • Complete solu1on combining state-of-the-art MD engine, sampling algorithm, force field, so`ware engineering, and GPU support for unparalleled accuracy, throughput, and ease of use in real-life prospec1ve drug discovery projects – A rou1ne part of the porgolio of design tools used by internal Schrödinger drug discovery group – Significantly enriching 1ght binders in all prospec1ve studies – Con1nuously developed for accuracy and performance improvements – Validated across wide range of systems – Scoring each ligand requires roughly 1 GPU day of compute ?me

Desmond GPU • GPU compu1ng provides a significant advantage

Schrödinger FEP+ Retrospec've Accuracy • Over 200 ligands scored w/ iden1cal protocol • RMSE ≈ 1 kcal/mol, correla1ons appear predic1ve -4 50% 46.2% -5 45% -6 40% -7 35% ΔG FEP (kcal/mol) Percentage -8 30% 24.8% -9 25% BACE -10 20% CDK2 15.4% JNK1 15% -11 MCL1 7.4% 10% -12 6.2% P38 5% -13 PTB1B THROM 0% -14 TYK2 < 0.6 0.6-1.2 1.2-1.8 1.8-2.4 >2.4 -15 |ΔΔG FEP – ΔΔG Expt. | (kcal/mol) -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 ΔG Expt. (kcal/mol) FEP+ — L Wang, et al. JACS., 137:2695–2703. (2015) OPLS3 — E Harder, et al. JCTC., 12:281–296. (2016)

• Our goal is not merely to make accurate predic1ons • Rather, our goal is to drive discovery projects forward • Can we do this? Referenced paper: L Wang, et al. JACS., 137:2695–2703. (2015)

Collabora'on A—Extremely rapid op'miza'on of a new lead compound

Rapid op'miza'on of a HTS hit with FEP+ • A high-throughput screen iden1fied a weak inhibitor (80 μM) of a high-value target • Project team needed to decide if the inhibitor could be further op1mized to progress the project from lead iden1fica1on to lead op1miza1on • Collaborator had synthesized 73 molecules to improve the potency of the HTS hit, but none were sa1sfactory

Rapid op'miza'on of a HTS hit with FEP+ • A crystal structure of the inhibitor showed a highly unusual binding mode • Collaborator suspected that unusual binding mode might give the inhibitor high specificity, if potency could be op6mized • Schrödinger was given 1 week to determine if there was any viable route to improve the molecule

Rapid op'miza'on of a HTS hit with FEP+ • Using in-house and cloud compu1ng resource ~ 3500 synthe1cally plausible deriva1ve molecules were scored – ~100K GPU hours • Only 23 of these molecules (0.6%) were predicted by FEP+ to boost the binding potency of the HTS hit • The collaborator chose to ini1ally only synthesize 3 of the recommended 23 molecules – Synthesis costs, if the molecules are challenging, can easily exceed $5,000 per molecule – Synthesizing all 3,500 would not be feasible for most discovery projects

Rapid op'miza'on of a HTS hit with FEP+ Molecule R-group FEP+ Ki (μM) FEP+ rank MMGBSA rank Expt. Ki (μM) A 0.6 1 427 26* B 1.8 2 95 3.8 C 3.2 3 435 2.2 HTS hit - - - 80 * Mixture with 3 stereocenters, frac1on of eutomer may be << 1/8

Rapid op'miza'on of a HTS hit with FEP+ • Using FEP+ to guide compound synthesis, the potency of the HTS hit was improved 40x in a single round of chemistry • Using simple MM-GB/SA scoring the top 435 compounds would have needed to be synthesized to recover these top 3 compounds • Further op1miza1on of molecules B and C is now also in progress

Collabora'on B—simultaneous op'miza'on of potency, selec'vity, and solubility

FEP+ use case from discovery collabora'on B • Prior to the ini1a1on of a large-scale FEP screening Expt. End Point Mol. A Mol. B Mol. C Mol. D campaign (June 2015), no molecules had been iden1fied which simultaneously achieved high potency, selec1vity, and solubility Potency ✔ ✔ ✔ ✔ – Many molecules achieved par1al success, but no (pKi > 9) molecules were sa1sfactory across all four criteria • Star1ng in June 2015, an unprecedented FEP Selec'vity 1 ✔ ✔ ✗ ✗ scoring campaign was ini1ated to find sa1sfactory ( > 100x ) molecules – ~4000 molecules scored by FEP to date (April 2016) Selec'vity 2 ✔ ✗ ✔ ✔ – Equivalent to > 5 years of wet-lab experimental ( > 100x) chemistry to test all scored idea molecules • Goal was to marry expert molecular design with Solubility ✗ ✔ ✔ ✗ predic1ve scoring to enable the undertaking of ( > 20 uM) challenging synthe1c targets

FEP+ use case from discovery collabora'on B • The FEP scoring campaign has been hugely successful, with 10 molecules now simultaneously mee1ng potency, selec1vity and solubility goals: FEP Date Potency Selectivity 1 Selectivity 2 Solubility Molecule Recommended Synthesized (pKi > 9) ( > 100x ) ( > 100x) ( > 20 uM) ✔ ✔ ✔ ✔ Mol. E Yes 11/4/15 ✔ ✔ ✔ ✔ Mol. F Yes 12/10/15 ✔ ✔ ✔ ✔ Mol. G Yes 12/10/15 ✔ ✔ ✔ ✔ Mol. H Yes 12/17/15 ✔ ✔ ✔ ✔ Mol. I No (Charged) 12/24/15 ✔ ✔ ✔ ✔ Mol. J Yes 12/28/15 ✔ ✔ ✔ ✔ Mol. K Yes 1/15/16 ✔ ✔ ✔ ✔ Mol. L Yes 1/21/16 ✔ ✔ ✔ ✔ Mol. M Yes 1/21/16 ✔ ✔ ✔ ✔ Mol. N Yes 3/16/16 • 46 FEP-r 46 FEP-recommended compounds wer ecommended compounds were synthesized and 9 checked all four boxes e synthesized and 9 checked all four boxes

FEP+ use case from discovery collabora'on B • The parent compound (Mol. D) of molecules E, F, and G was neither highly selec1ve nor soluble: Mol. D: Mol. E, F and G: pKi > 9 pKi > 9 Selec. < 100x Selec. > 100x Solub. < 10 µM Solub. > 20 µM Core Core Core Core • Molecules A, B and C were from a different subseries, and could not be related to molecules D, E, F or G through FEP+ perturba1ons – Parallel large-scale op1miza1on of mul1ple subseries was essen1al to the iden1fica1on of molecules E, F, and G – These molecules were synthe1cally challenging due to the chemistry of the core, and were unlikely to have been synthesized without FEP+ scoring

FEP+ should dras'cally expand the chemical space explored by discovery project teams • True impact of FEP+ will not be seen un1l investment in FEP is comparable to other drug discovery project costs – Ideally, many thousands of FEP calcula1ons per project – In effect, one is able to run a ~100,000 compound medicinal chemistry project in less than a year – 100x increase in throughput over a typical discovery project • Such predic1ve scoring should enable much more rapid op1miza1on and balancing of ligand proper1es than would be otherwise possible in lead op1miza1on • Increasing the success of preclinical drug discovery may be the most promising avenue to be]er meet the urgent need for more effec1ve drug therapies

Acknowledgements Scien'fic Development Scien'fic Advisors Applica'ons Sciences Leadership Wolfgang Damn Bruce Berne Thijs Beuming Ramy Farid Yuqing Deng John Chodera Daniel Cappel Ed Harder Rich Friesner Osamu Ichihara Drug Discovery Joe Kaus Bill Jorgensen Roy Kimura Group Byungchan Kim David Mobley Ana Negri Leah Frye Jen Knight Vijay Pande Daniel Robinson Sarah Boyce Goran Krilov Mark Murcko Woody Sherman Mark Brewer David Lebard Dima Lupyan Thomas Steinbrecher Sathesh Bhat Sayan Mondal D. E. Shaw Research Jonathan Gable Lingle Wang Michael Bergdorf Jeremy Greenwood Chuanjie Wu Jus1n Gullingsrud Kyle Konze Yujie Wu Ross Lippert Shaughn Robinson Yutong Zhao Charles Rendleman Markus Dahlgren Chongkai Zhu Danielle White Fiona McRobb Huafeng Xu And enormous thanks to our collaborators!

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