Innovative Approaches to Computational Drug Discovery

October 1, 2013 to October 4, 2013
Location : CECAM-HQ-EPFL, Lausanne, Switzerland
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  • Marco De Vivo (Istituto Italiano di Technologia, Italy)
  • Matteo Dal Peraro (Swiss Federal Institute of Technology Lausanne (EPFL) , Switzerland)
  • Andrea Cavalli (Istituto Italiano di Tecnologia and University of Bologna, Italy)



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We have received an overwhelming number of applications, while the  allowed number of participants is limited. We apologize if you did not receive a response after your application. 


if you did not receive a positive response to your appplication, please consider yourself in the waiting list. In case new spots become available, we will contact you. 

Thank you very much for your interest. 

The organizers.


There is no fee to attend to the workshop. However, the number of participants is limited. Participants will be selected by the organizers. When applying, please provide a sceintific abstract of your poster, and motivations. - Thank you.

A few poster proposals will be selected for a short oral presentation.
Also, at the end of the workshop, one poster will be awarded.

Acceptance and selected posters for short talks will be communicated ASAP, and anyway before June, 2013.

At this point, we have no information on money available to support travel/accomodation expences of young students. More on this will come later. 

See you in Lausanne!
The Organizers.


We propose a workshop on innovative approaches to computational drug discovery, which follows up on the first workshop on the same topic we organized at CECAM in 2011. The 2011’s workshop was a great success, which has involved more than 50 attendees, has allowed collecting generous support from a number of external sponsors, and has seen the participation of several eminent speakers from all over Europe and the US. Among others, we name Prof. W. L. Jorgensen (Yale), who delivered the opening lecture in 2011 and who has already confirmed his participation to the workshop proposed here, in 2013. Prof. M. Parrinello (ETH) has also confirmed his participation, as well as many other prestigious speakers such as, to nominate just a few, Prof. B. Shoichet (UCSF), Prof. K. Merz (Univ. of Florida), Prof. R. Abagyan (UCSD), Prof. V. Pande (Stanford University), Prof. G. Klebe (Philipps-Universität Marburg), and others (see complete list in “participant list”). Indeed, the 2011’s workshop has received very positive feedbacks from all participants, on both the scientific quality of the discussions held, as well as on the general organization of the event. Already during the 2011’s workshop became clear the interest in a second event to be held at CECAM, which we therefore propose here.

In this second edition, we have integrated topics that are, and will remain, central to the field, such as structure-based drug discovery and molecular simulations for drug design, with the introduction of novel aspects that are nowadays among the hot topics in the field of drug discovery. In particular, we will focus on the role of computational systems biology in target selection and drug-like profile optimization (1). Also, we intend to dedicate much attention to the important topic of novel targets for drug discovery. The search of novel targets for drug discovery is indeed a major need, while the definition of an “effective” novel target is quite different from academia and industry (2). In this regard, the discussion of these topics will greatly benefit from an audience that will be formed by a mixture of scientists from industry and academia.

Therefore, the ultimate aim of the proposed workshop is to discuss and update the computational community on innovative approaches nowadays available for drug discovery. We will touch all the major aspects that today represent the challenges in the field, with many great speakers/scientists of the computational drug discovery community. Also, we will facilitate the interaction of researchers favoring discussions that bridge the gap between calculations and experiments needed to guide the drug discovery process.

Computational drug discovery approaches have multiple facets. The available physicochemical information dictates what strategy is more likely to be applied. Where structural data of the target protein exist, structure-based drug design (SBDD) is by far the most applied strategy. However, when this structural information is missing, or not fully reliable (e.g., homology models of the target, based on poor sequence identity), ligand-based approaches are usually utilized. These latter approaches are also usefully deployed when biological information is available for a large number of compounds. For instance, toxicity data for hundreds of thousands of molecules can be used to extract mathematical models for early toxicity prediction of novel drug candidates. Simulations of complex network systems where large-scale genomics, proteomics and metabolomics measurements are duly integrated at the cellular and organ level have recently emerged as an efficient strategy for aiding synthesis route prediction, target and compound validation, and clinical drug selection. Clearly, a computational chemist wishing to succeed in drug discovery must be familiar with the full variety of computational approaches available (3).

Several methods can be considered as part of the computational armamentarium, spanning from docking calculations to classical molecular dynamics (MD), from pharmacokinetic profiling to stochastic and/or deterministic chemical reaction methods. However, each method has its limitations. Docking is quite good at correctly posing compounds into the cavity, but scoring remains a major issue. For instance, a recent application of steered MD has been able to rank compounds, and to discern active from inactive enzyme inhibitors (4, 5). This seems a promising strategy for hit identification. Nevertheless, a major challenge of computation in predicting active compounds is to find the right balance between accuracy and speed. Nowadays, CPU-intensive calculations, such as free energy perturbation (FEP), can be used to accurately estimate the binding free energy (6). However, the sampling and convergence of the results are not always complete, and it is still difficult to handle large structural differences between ligands. Quantum mechanics (QM) has recently shown itself ready to make an impact on SBDD (7-10). From QM-based scoring function, to QM studies of enzymatic mechanisms for transition state analogues design (11), QM methods now have real potential for drug discovery. From statistical mechanics to quantum mechanics, physics-based methods can play a major role in improving the performance and predictivity of computational drug discovery methods (12). Nonetheless, depending on the stage of the project, other issues might arise, which are not related to the binding of the drug to the target. For instance, the physicochemical properties of active compounds dictate their pharmacokinetics (PK) profile. Adsorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) are key parameters to be tuned in order to produce a drug candidate with a drug-like PK profile, thus that including ADMET prediction at early stages can prevent the possibility that a novel drug candidate will fail in the later costly clinical phases. Abandoning the molecular perspective and considering the whole network of interactions of drugs and their targets have shown recently to have a high impact on the drug discovery pipeline. The search for optimal pathways at the system level is in fact key for prioritization of targets and design of drug clinical trials, with the final result of developing therapeutics with higher efficacy and fewer side effects. These major aspects of computation applied to drug discovery will be discussed during the workshop by an audience that will include both academic and industrial scientists active in all these different domains.


[1] Butcher EC, Berg EL, & Kunkel EJ (2004) Systems biology in drug discovery. (Translated from eng) Nat Biotechnol 22(10):1253-1259.
[2] Mullard A (2011) Reliability of 'new drug target' claims called into question. Nat Rev Drug Discov 10(9):643-644.
[3] Jorgensen WL (2004) The many roles of computation in drug discovery. Science 303(5665):1813-1818.
[4] Colizzi F, Perozzo R, Scapozza L, Recanatini M, & Cavalli A (2010) Single-molecule pulling simulations can discern active from inactive enzyme inhibitors. J Am Chem Soc 132(21):7361-7371.
[5] Jorgensen WL (2010) Drug discovery: Pulled from a protein's embrace. Nature 466(7302):42-43.
[6] Jorgensen WL (2009) Efficient drug lead discovery and optimization. Acc Chem Res 42(6):724-733.
[7] Cavalli A, Carloni P, & Recanatini M (2006) Target-related applications of first principles quantum chemical methods in drug design. Chem Rev 106(9):3497-3519.
[8] Dal Peraro M, Ruggerone P, Raugei S, Gervasio FL, & Carloni P (2007) Investigating biological systems using first principles Car-Parrinello molecular dynamics simulations. Curr Opin Struct Biol 17(2):149-156.
[9] Raha K, et al. (2007) The role of quantum mechanics in structure-based drug design. Drug Discovery Today 12(17-18):725-731.
[10] Zhou T, Huang D, & Caflisch A (2010) Quantum mechanical methods for drug design. (Translated from eng) Curr Top Med Chem 10(1):33-45.
[11] De Vivo M, Dal Peraro M, & Klein ML (2008) Phosphodiester cleavage in ribonuclease H occurs via an associative two-metal-aided catalytic mechanism. Journal of the American Chemical Society 130(33):10955-10962.
[12] Deng Y & Roux B (2009) Computations of standard binding free energies with molecular dynamics simulations. J Phys Chem B 113(8):2234-2246.