 I have a classical example of lead optimization I want to show you. This is the HIB1 protease. It's a key protein in the HIV virus that fulfills the same role as the protease in SARS roughly. So this is important for the virus to cut up its own proteins and infect the host cell. If we could kill the function of this protein we would be able to combat the HIV virus. So in this case they started to roughly design a pharmacophore although here it's shown as a molecule. This is not the chemistry class so forget about what I'm calling these molecules but I need to describe roughly what it is. So this is a symmetric mirror image diol to alcohols and it had some activity. Then we created a pharmacophore from that well not me personally but the field. Do you see here the schematic representation? Based on that pharmacophore you found hits in databases. So this is a particular database hit but it's too large and a few too many groups. It's certainly not ideal. Based on that database hit people created a simpler design and here you don't draw the entire side groups. So based on their knowledge of chemistry and the molecule and everything we hope that this should fit the size of the cavity in the middle here better and also have nice binding properties that should bind those red side chains in particular. In the next space you extend this to make it a diol again and you add a bit of urea. I actually don't remember why they added urea. Obviously they hope that it would bind better. Then you optimize the stereochemistry for binding and there are probably another 20 stages in between here that I don't show you and in each iteration here they would have to do additional tests and see that this is improving it or deteriorating it and I bet they didn't show the stage that deteriorated it. There is a lot of trial and error here. Based on all these iterations eventually they came up with this molecule that is somewhat reminiscent of the original molecule but quite a bit different right. This molecule is the HIV1 protease inhibitor and this was the first ever really computationally designed drug on the market. This is saying countless of lives because this is one of the early HIV break medicines so we can't really treat HIV right but we can postpone the whole infection make it slow down so much that you basically live the rest of your life. The problem is that this virus is very good at mutating so once you're starting adding this you're going to create a selective pressure that if this molecule binds in the middle of the HIV1 protease the protease might start to change those red side chains or the purple ones there to make it more difficult for this molecule to bind. Again purely by chance of course but viruses with that mutant will have an increased probability of surviving. So people have had to develop new HIV1 protease inhibitors improve the existing ones and do the same thing for a ton of other molecules fighting HIV1 too. The problem with HIV is that it's a very rapidly mutating virus. Thank god SARS-CoV-2 appears to be a much slower virus and it's much easier to say for instance design a vaccine against the SARS virus.