Foremost we want to accomodate our clients interests. So please reach out with other suggestions of projects also, that are more inline with the work made in your workplace, Lab or industry company, to discuss how we can compliment that work.

Example projects we welcome collaboration and apply funding for:

Please reach out if interest to collaborate, work, or fund some example research projects described here.

Projects will be described in more detail as time progresses, some are "overview" defined, anyone can give suggestions to improve and refine any project objective. There are other possible projects not included yet on this website. (Articles cited represents direction and knowledge and information, a few are published from people in this lab.)

Drug discovery, Bioinformatics, Biomolecule Modelling and Automation pipe-lines

The increase of databases, machine-learning and automation, has potential to discover new drugs for use in health and medicine. We have wide literature knowledge and ability to work on ML and automation models for drug discovery pipe-lines. We are more prone to believe natural compounds have clues to drug discovery, though of course purely synthetic compounds have potential to treat various diseases.

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. and Blaschke, T., 2018. The rise of deep learning in drug discovery.Drug discovery today,23(6), pp.1241-1250.

Khan, S.R., Al Rijjal, D., Piro, A. and Wheeler, M.B., 2021. AI-integration and plant-based traditional medicine for drug discovery. Drug discovery today.

Abstractation of molecules and macro-molecules for processing in ML models.

David, L., Thakkar, A., Mercado, R. and Engkvist, O., 2020. Molecular representations in AI-driven drug discovery: a review and practical guide. Journal of Cheminformatics, 12(1), pp.1-22.

Graph neural networks and biological network analysis

Muzio, G., O’Bray, L. and Borgwardt, K., 2021. Biological network analysis with deep learning.Briefings in bioinformatics,22(2), pp.1515-1530.

De novo molecule library generation

Meyers, J., et al., 2021. De novo molecular design and generative models.Drug Discovery Today,26(11), pp.2707-2715.

Polishchuk, P., 2020. CReM: chemically reasonable mutations framework for structure generation.Journal of Cheminformatics,12(1), pp.1-18.

Molecular Dynamics and quantum chemistry modelling of drug candidate leads.

Wittler, H.P.A., 2023.A Molecular Dynamics Analysis of Insulin(Doctoral dissertation, La Trobe).

Zaidman, D., et al., 2021. An automatic pipeline for the design of irreversible derivatives identifies a potent SARS-CoV-2 Mpro inhibitor.Cell chemical biology,28(12), pp.1795-1806.

Beneath the biological surface

- is biochemical life described today by Evolution or "Devolution and Adaptation" ?

- is biochemical life irreducibly complex ?

- is current origin of life scenarios believable by empirical chemistry we know of today ?

Confirming observations about if we today observe devolution and adaptation, and if life is irreducibly complex

Bioinformatic research related to paradigms of origin and behaviour of life. Projects relating to if the paradigm of devolution and adaptation is more realistic than the for decades conventional paradigm of evolution. To at least ask the question what do we actually observe in the present, since we cannot directly observe what were the case thousands or even if postulating millions of years ago.
Moreover the paradigm of devolution and adaptation states that observable foundational information coded in biochemical systems that we measure (at least in the nearest time and generations of life), is not increasing over time, rather decreases in quality over time even across generations of life (see e.g. Kondrashov 1995). 
There are very strong indications that irreducible complexity in biochemical machinery is empirically certain. Irreducible complexity can be likened to text in books, where it can be lost some letters here and there, and the text can still be functioning in the book, however if too many letters, words or sentences are removed some function is lost. Irreproducible complexity is yet to be proven mathematically with aid of machine-learning and the growing bioinformatics databases; if proven it has profound implications on how we perceive life, biochemistry, and pharmacology.

To emphasize: This paradigm clarification could have profound importance how we today perceive pharmacology and how we treat damaged biochemical systems that are the cause of diseases, such as various syndromes, diabetes and cancer types (examples of handicaps or diseases caused by broken biochemical systems).

Many in academia, would agree that, for example, Michael Behe ​​(https://michaelbehe.com/), John Sanford, James Tour (for questioning current abiogenesis theory) and countless others can be a modern Copernicus (see Mini Bio documentary), whose scientific observations can reshape a firmly held worldview of the doctrine of evolution (a religion in some sense since it takes a lot of faith to believe the fundamental assumptions, e.g. about abiogenesis theory). That we may actually be observing devolution and adaptation within species of life. We all know that earthly life is ultimately moving in the direction of aging and dying every day, most biochemists/genetisists know this, however only a few have verifiedly studied genetics and biochemistry to also observe that this appears to be a phenomenon across generations of species (at least as measured in the nearest time as of today).

Particular considerations made when investigating this.

- Design paradigm, computer analogue similarity of biochemical life.

- Ubiquotous "unidirection" or homochirality of biochemical molecules, in addition to the symmetric and mosaic dynamical nature of biochemical life, these aspects being empirically known.

- Fine-tuning of biochemical systems, and perturbations in information or structure to biochemical systems.

- The measured increase of mutational damage over generations, i.e. Genetic Entropy (Basener, Sanford 2018 ; Kondrashov, 1995; Carter and Sanford 2012).

- The statistical implausibility of biochemical life and order to investigate. Observe that the combinatorial complexity of cells (sequence-order of DNA, protein-protein interactions, protein-RNA interactions etc) reaches basically infinity and whose biochemical systems are comparable to human code, linguistics and writing. Another observation is that the combinatorial complexity of all letters and characters in all books of a library is also closing in to immensely large numbers. See references (Yockey 1981, Tompa and Rose 2011).

Basener, W. F., Sanford, J. C., 2018. The fundamental theorem of natural selection with mutations. Journal of mathematical biology, 76(7), 1589-1622.https://doi.org/10.1007/s00285-017-1190-x

Kondrashov, A.S., 1995. Contamination of the genome by very slightly deleterious mutations: why have we not died 100 times over?. Journal of theoretical biology, 175(4), pp.583-594.https://doi.org/10.1006/jtbi.1995.0167.

Carter, R.W. and Sanford, J.C., 2012. A new look at an old virus: patterns of mutation accumulation in the human H1N1 influenza virus since 1918.Theoretical Biology and Medical Modelling,9(1), pp.1-19.https://doi.org/10.1186/1742-4682-9-42

Yockey, H.P., 1981. Self organization origin of life scenarios and information theory. Journal of Theoretical Biology,91(1), pp.13-31. https://doi.org/10.1016/0022-5193(81)90370-2 

Tompa, P. and Rose, G.D., 2011. The Levinthal paradox of the interactome.Protein Science,20(12), pp.2074-2079. 

A course on the weak grounds for belief in abiogenesis theory as of known empirical chemistry - by Dr James Tour