Biochemical Infometrics


• Biographical details
• Collaborative experience
• Publications
• Patents


"Molecular design of biologically active small is intrinsically complicated and hard, and it is getting harder and more complicated every day. For one thing, it is inherently multi-objective, with improvement in one attribute - e.g., potency - often coming at the expense of another - e.g., bioavailability. Moreover, attributes such as half-life exhibit optima as well as trends, requiring a careful balancing act between being cleared too quickly and being cleared too slowly.

Given that complexity, it makes sense to bring as many disciplines to bear on the problem as possible; cranking blindly through black box models or a synthetic strategy of methyl, ethyl, butyl, futyl ... will not get the job done. That is why drug discovery and development has evolved into a team activity, with people from many disciplines working together. 

Biochemical Infometrics was founded in 2008 as a way to make my drug design experience available to smaller companies and it closed down in January 2010 when I became Director of Life Sciences at Simulations Plus, Inc. My time there afforded me many great opportunities to apply my inter- and trans-disciplinary perspective to challenging modeling problems in DMPK and predictive toxicology. Retiring from Simulations Plus on April Fool's Day in 2022 has given me more freedom to write, to collaborate, and to explore new approaches to old problems. My Adjunct Professorship in Informatics at Indiana University Bloomington provides some excellent opportunities for doing that, but I am also resurrecting Biochemical Informatics as a scientific consulting group - albeit a nonprofit one, at least for the time being."

Oral Presentation
20 August 2022
Fall National ACS Meeting in Chicago

AI-driven drug design (AIDD) 

Coupling high-throughput pharmacokinetic simulation (HTPK) to multi-objective molecular evolution of triazolopyrimidine antimalarial leads

De novo drug design is a long-standing challenge in computational chemistry. Much recent work has centered on using generative neural networks and related technologies to create chemical libraries that reproduce property &or structural distributions that mimic those of compounds known to be active against a selected target. The AI-driven Drug Design (AIDD) module in ADMET Predictorฎ takes a different approach: in AIDD, one or more molecules are submitted to randomly chosen SMIRKS transformations, yielding products that are then evaluated against a panel of structural SMARTS filters and objective functions. Those that pass the filters and are Pareto optimal with respect to the objectives and to the prior generation of analogs are carried forward as the next generation, which is itself subject to additional transformations, and so on.

The case study to be presented here involves a series of triazolopyrimidines (TzPs) that inhibit the dihydroorotate dehydrogenase of the malarial parasite, Plasmodium falciparum (PfDHODH) [1]. Examples from very early in the synthesis program were input to AIDD as structural seeds from which to evolve several series of analogs optimized against four objective functions: a general artificial neural net ensemble (ANNE) model of PfDHODH inhibition with a single hidden layer of neurons [2]; bioavailability estimates obtained by high-throughput pharmacokinetic (HTPK) simulation; synthetic difficulty estimates; and a set of absorption, distribution, metabolism, excretion and toxicity (ADMET) property estimates encoded as a weighted set of rules (AIDD Risk).

The output analogs obtained afforded good structural coverage of the activity space, were novel but synthetically reasonable. They included several that were actually synthesized later in the TzP program. Those subsequently synthesized analogs included several that were not in the data set used to build the ANNE activity model. Hence this case study is largely prospective in nature despite being drawn from the literature.

1. M.A. Phillips et al., J. Med. Chem. 2008, 51, 3649–3653.
2. Clark et al., J. Comput.-Aided Drug Des. 2020, 34, 1117-1132.

Hedge Your Bets to Come Out Ahead

If you watch the people who make a living at racetracks, you will see that they do not pick winners.  Instead, they typically bet on several horses to Win, Place or Show.  In many ways, drug development is a similar game, in that you are operating with limited and imperfect information and chance plays a big part in how your corporate gambles turn out. 

Intelligent molecular design can add value - a lot of value - by helping you spread your risk rationally across leads and candidates.  Most people already appreciate the importance of cultivating structural diversity and synthetic accessibility when picking compounds for follow-up acquisition or synthesis, but there is as much or more to be gained by diversifying across likely ADMET and PK risks as well.  Being able to make relevant and reliable predictions of the corresponding  properties within the realm of well-explored chemistry is critical.  Such predictions are still something of an art when one ventures into new areas, but that is where the value added is.

ฉ 2022 Biochemical Infometrics; all rights reserved.

                  Design Consultants Group

Skill Base

•  predictive uncertainty
•  predictive ADME/Tox
•  QSAR & statistics
•  library design
 •  biometrics

•  synthesis
•  biochemistry
•  cheminformatics
•  intellectual property