Great interview for a great PLOS ONE article! Many thanks!

My take away is that this is what the so-called Systems Biology / in silico Biology ppl have been talking about for the past 10 years or so. Unfortunately the drug discovery industry has its own systematic problems as A) most major biopharma are run by MBA/lawyers/D. Pharm, (this is why M&A / tax inversion / outsourcing have higher priority than basic R&D); B) from A the basic R&D (left along Systems Biology) are on the chopping block for cost cutting; C) and it's hard for Bio Data Hacking companies to survive in the past 10 years or so. Most bioinformatic companies are either out of business or being absorbed. The Entelos the authors mentioned actually went through IPO, relisting, chapter 11 and recapitalization.

So my question would be how these road blocks could be overcome before in silico biology could contribute more to the drug discovery process for a better "compass"?

The article's conclusion would indicate that you would do use in-silico biology to 'discover' existing drugs. Then once computer methods can reliably discover, say both penicillin and tetracycline, you'd push forward.

Funding this, however, would never come from industry. You'd need to convince NIH that spending many years rediscovering old drugs is the magic leap forward we need.

As someone ignorant of the field - has this been done? What is the state of validation in computer biology?

yeah, more or less. it's called drug repositioning for 're-discovering' existing drugs for new indications. several firms are currently focusing on drug repositioning by mining fda databases such as drug adverse events data to mine possible new indications. major pharmas already use the find-new-trick-for-old-drug to find new usage (eg botax). by the way viagra is also such 're-discovered' drug as it was originally targeted for hypertension treatment with poor clinical trial results but with a surprising you-know-what 'side-effect' in some male patients.

any way, the solution might not be so straight forward for in-silico biology as several major bottlenecks still hard to resolved, such as those failed clinical trials data are never revealed and shared, and the most cash-burning process still concentrate on clinical trial stages.

See earlier comment by me.