Field of Science


Drugs and rational drug discovery







Structure-based drug design and CADD




Virtual Screening



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Binding affinity prediction



Models, computers and drug discovery








  1. Nice article. One point -- the sulfur-carbonyl cis preference is actually fairly well known from small-molecule xray structures. See e.g. for a review

  2. A great review of the state of computer simulations…No offense to anyone, but an important distinction must be made between in-silico science users and coding authors. When I say 'coding authors' I don't mean computer scientists who know enough physical science to be dangerous, but rather, physical scientists who author algorithms based upon first principle laws of physics. They may not write the fastest code (leave that up to the computer scientists to optimize) but they can track their code to its theoretical underpinnings making adjustments as both theory and experiment evolve. As a molecular dynamics engine coding author (and there are only a handful around) I can tell you that there are many users and programmers that consider themselves coding authors that do not have the theoretical horsepower to be taking lead roles in computer simulation coding forums. You are correct about the models - they are the problem. Computer speed is not going to solve the fundamental problems we have with inaccurate in-silico simulations - realism is the key. When models are not realistic, fast computers simply get to the wrong answer sooner. In molecular dynamics, bridging the real world to the model world is by nature an attempt to solve the many-body problem. This is where most coders give up because force potentials can only be deductively evaluated. It becomes the same dilemma as quantum mechanical simulations - the larger the system, the more intractable the problem. And so, is there an inductive approach to this? Yes there is - but one needs to know the theoretical underpinnings of force fields in order to make the algorithmic adjustments. But this is almost a social problem. Who would appreciate its solution? Most of the popular pseudo-coding authors are running the show (forum moderators) and wouldn't know the real deal if it fell on them. So who suffers? We all do because until there is realism in computer simulations, we are looking at glorified video games - nothing more. Assume for a moment that a supreme being handed you the "real" force functions for your computer simulation, wouldn't you expect that the simulation might become realistic? Force functions are the bricks in the wall of molecular structure and behavior in simulations. If the forces are correct, the simulation is real because the mode mechanics are real and consequently, self-assembly will be real. Ask yourself, what are the implications if computer simulations of the weather were 100% accurate? It’s scary in some respects. How much does realism speed up the R&D process? Think about it.

  3. Anon 1: Thanks for the wonders why the original authors did not realize this.

    Anon 2: Thanks for your articulate thoughts. You are quite right that ultimately the theoretical underpinnings decide the model. Think of the several schemes for denoting charge and electrostatics that go into modeling. Which one of these is the best? I have not yet discovered any one force field which can model energies accurately. Ultimately I think of the computer as an "eliminator" which, even if it knocks down straw men, will be valuable. I have also realized that many coders don't have a handle on empirical and physical data which they can use to improve their models and explore the presence of outliers. There still has to be very good synergy and cross talk between coders, applications scientists and actual experimentalists. In the end it's the accurate experimental number that is paramount.

  4. This is anon 2. There is no single force field that will model everything. Please take this constructively...there is, however, a way to find the force field 'on-the-fly' as you need it. I'm walking around this for my owns reasons, but take the lame analogy that force fields are like happiness...not a single event, but the results of a process. The process of finding theoretically transparent or theoretically opaque force functions does exist. I know this is quite a claim, but think about the premise that one must know some superfunction first and then deductively guess (through molecular dynamics experiment) the real outcome. We do this because it is the way things have been done since day one. It's been the only game in town and people don’t like radical change – it makes them feel bad – and yes, that is their problem, but there are so many of them. Anyway, I like to refer to the existing methodology as 'playing God'. You (the general 'you' not “you” you) create the fundamental building block of the simulation (the force function(s)), and expect the simulation to deductively reflect the behavior of the real world. That is an ego trip, really. All of us in-silico scientists need humility about this. Only the real world knows itself and is capable of correcting a model to be like itself. I'm talking about a feedback mechanism from the real world to the model world. It does exist. Yes, there is a way to bridge real experiments to model experiments inductively then correct the models on-the-fly (until there is optimal realism convergence). And yes, it smells like I just said that I knew how to solve the n-body problem analytically – I never said analytically – however, you do get a theoretically transparent or theoretically opaque force field out of this process without having to play God. Let nature play God through a feedback mechanism. And finally, “yes” details are available.

  5. I think you'll find ligand based design has been around rather longer than you give it credit for. This field didn't start with shape matching.

    Also I don't think it is correct to state that the binding enthalpy and entropy are evaluated at each step in TI or FEP. A free energy (Helmholtz or Gibbs depending on ensemble) associated with mutations is what is normally calculated. You need to break the mutation into smaller steps otherwise the simulations don't converge.

  6. Great post ! The overview of the field I was looking for without the optimism of the protagonists of each method, and with all the warts exposed.

    Can one actually calculate the potential energy surface mentioned in the paragraph after pose prediction? The caveats about models are particularly good.

    Nonetheless, this stuff is really important, even though it doesn't work as well as we'd like.

    Consider the situation with the Mexican flu virus. The molecular biologists have already cloned and sequenced its genome. They likely know exactly where the mutations are that make this virus different (or the new assortment of genes from different species making up the new virus).

    Also consider how poorly we understand protein chemistry (and small molecule chemistry). If we really understood protein chemistry, theoreticians would have predicted all the new altered conformations of the new proteins by now, and computational chemists would have designed a small bioavailable molecule to inhibit those conformations, and synthetic chemists would have made tons of them. None of the last 3 desiderata is anywhere close to reality, which is why chemistry is more than just intellectually fascinating and a thing of beauty.


  7. Sorry for misusing the comment function - I'd like to get in contact to you. How can I reach you?

  8. @Anon2: Nice analogy with happiness. A little sadness makes the picture complete. IMHO coders need to know more physical science than physical scientists need to know coding. Simply making algorithms more efficient and adding more parameters is surely not the ultimate way to nirvana.

    @Retread: Thanks, and you make a cogent point about H1N1 and our primitive knowledge of protein function. The hemagglutinin molecule does some amazing acrobatics at reduced pH whereby it enables fusion. I would have thought someone would have designed a potent and effective hemagglutinin inhibitor. To my knowledge the current drugs only target the neuraminidase and M2 proteins. Perhaps the lack of a hemagglutinin inhibitor is not surprising; protein-protein interactions, anyone? A colleague works on inhibiting the measles fusion protein and I can see his frustration in spite of many attempts at inhibitor discovery.

    As for the PES, it's not done rigorously. It's essentially a quick and dirty Monte-Carlo conformational search with extensive knowledge-based evaluation involved. QM/MM calculations could possibly do it though. QM calculations will take the course of our natural lifetimes.

    @GMC: LBD certainly went beyond shape-based. I just think shape-based is one of the best current methods for LBD. There's of course nothing as delicious as having a well-resolved structure. As for FEP, I think I should have accurately stated that PMFs are often calculated.

    @Beatrice: No problem. Get in touch at


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